diff --git a/hatch_build.py b/hatch_build.py
index 0bacf21..a529613 100644
--- a/hatch_build.py
+++ b/hatch_build.py
@@ -39,6 +39,7 @@
     "packages/catalog/src/mdl_catalog": "mdl_catalog",
     "packages/server/src/mdl_server": "mdl_server",
     "packages/lsp/src/mdl_lsp": "mdl_lsp",
+    "packages/mcp/src/mdl_mcp": "mdl_mcp",
     "packages/adapters/collibra/src/mdl_adapter_collibra": "mdl_adapter_collibra",
 }
 
diff --git a/packages/cli/pyproject.toml b/packages/cli/pyproject.toml
index 72a60ca..bc1ab74 100644
--- a/packages/cli/pyproject.toml
+++ b/packages/cli/pyproject.toml
@@ -18,6 +18,7 @@ dependencies = [
     "modelith-adapter-collibra",
     "modelith-server",
     "modelith-lsp",
+    "modelith-mcp",
     "typer>=0.12",
 ]
 
@@ -45,3 +46,4 @@ modelith-catalog = { workspace = true }
 modelith-adapter-collibra = { workspace = true }
 modelith-server = { workspace = true }
 modelith-lsp = { workspace = true }
+modelith-mcp = { workspace = true }
diff --git a/packages/cli/src/mdl_cli/main.py b/packages/cli/src/mdl_cli/main.py
index 7b77c1d..1bda4f3 100644
--- a/packages/cli/src/mdl_cli/main.py
+++ b/packages/cli/src/mdl_cli/main.py
@@ -1677,6 +1677,80 @@ def lsp() -> None:
     lsp_main()
 
 
+model_app = typer.Typer(
+    help="Read the model as JSON (the shared query layer behind the AI surfaces)."
+)
+app.add_typer(model_app, name="model")
+
+
+@model_app.command("context")
+def model_context(model_dir: Path = typer.Option(Path("."), "--model-dir", "-m")) -> None:
+    """Print a condensed model summary (project, counts, subject areas, relationships)
+    as JSON — sized for a chat context window. Backs `@modelith` in VS Code Ask mode
+    and the MCP `get_model_context` tool."""
+    from mdl_core.query import get_model_context
+
+    typer.echo(json.dumps(get_model_context(_load(model_dir).model), default=str))
+
+
+@model_app.command("entities")
+def model_entities(
+    model_dir: Path = typer.Option(Path("."), "--model-dir", "-m"),
+    subject_area: str = typer.Option(None, "--subject-area", help="Scope by area name or ULID"),
+) -> None:
+    """List logical entities (name, definition, attribute count, subject area) as JSON."""
+    from mdl_core.query import list_entities
+
+    typer.echo(json.dumps(list_entities(_load(model_dir).model, subject_area), default=str))
+
+
+@model_app.command("entity")
+def model_entity(
+    name: str = typer.Argument(..., help="Entity name or ULID"),
+    model_dir: Path = typer.Option(Path("."), "--model-dir", "-m"),
+) -> None:
+    """Print one entity's full detail (attributes, keys, conceptual layer,
+    relationships) as JSON. Exits 1 with an error object if no entity matches."""
+    from mdl_core.query import get_entity
+
+    hit = get_entity(_load(model_dir).model, name)
+    if hit is None:
+        typer.echo(json.dumps({"error": f"no entity named {name!r}"}))
+        raise typer.Exit(1)
+    typer.echo(json.dumps(hit, default=str))
+
+
+@model_app.command("detail")
+def model_detail(
+    model_dir: Path = typer.Option(Path("."), "--model-dir", "-m"),
+    limit: int = typer.Option(40, "--limit", help="Max entities (alphabetical) to include"),
+) -> None:
+    """Print compact detail for EVERY entity (attributes, keys, relationships) as JSON,
+    for grounding a model-wide question. Terse by design and capped at --limit, with a
+    `truncated` flag so a large model doesn't overflow a chat context window."""
+    from mdl_core.query import entities_detail
+
+    typer.echo(json.dumps(entities_detail(_load(model_dir).model, limit=limit), default=str))
+
+
+@app.command()
+def mcp(
+    repo: Path = typer.Option(
+        Path("."), "--repo", "-m", help="Model repo directory (contains mdl-project.yaml)"
+    ),
+) -> None:
+    """Start the Modelith MCP server (stdio) — model + ontology tools for an AI
+    agent (VS Code Copilot Chat in agent mode, Claude Desktop, Cursor).
+
+    Reads (list_entities, get_entity, search_ontology, get_model_context, validate)
+    and writes (create_entity, update_entity) share the same core query + command
+    engine as the CLI and canvas. Writes go straight to this checkout — the
+    engineer's own trust boundary, not the SME propose-as-PR flow."""
+    from mdl_mcp.server import run as mcp_run
+
+    mcp_run(repo)
+
+
 @app.command()
 def serve(
     model_dir: Path = typer.Option(Path("."), "--model-dir", "-m"),
diff --git a/packages/core/src/mdl_core/query.py b/packages/core/src/mdl_core/query.py
new file mode 100644
index 0000000..6461c9b
--- /dev/null
+++ b/packages/core/src/mdl_core/query.py
@@ -0,0 +1,276 @@
+"""The shared read/query layer for the AI surfaces (MCP server + chat participant).
+
+Both the VS Code MCP server (agent mode) and the `@modelith` chat participant
+answer questions from the same functions here, so the two frontends never drift.
+The shapes are deliberately CONDENSED — sized for a chat context window — not the
+full canvas projection (`mdl_server.projection`), which pre-joins everything the
+React graph needs. A grounding tool wants "what exists and how it connects" in a
+few hundred tokens, not the whole wire model.
+
+Everything here is a pure read over an in-memory `Model`; nothing loads or writes.
+The CLI/MCP wrappers do the `ModelRepo.load` and hand the model in.
+"""
+
+from __future__ import annotations
+
+from mdl_core.ir import ConceptualEntity, LogicalEntity, Model, Term
+
+
+def _entity_by_name(model: Model, name: str) -> LogicalEntity | None:
+    """Resolve a logical entity by name or ULID. There is no name index in the IR
+    (only ULID-keyed dicts and domain/code-set name lookups), so match by name
+    case-insensitively, then fall back to a ULID hit."""
+    lname = name.lower()
+    for le in model.logical_entities.values():
+        if le.name.lower() == lname:
+            return le
+    obj = model.logical_entities.get(name)
+    return obj
+
+
+def _ontology_summary(obj: ConceptualEntity | Term | LogicalEntity) -> str | None:
+    """The object's canonical alignment as a single prefixed IRI, or None. Reads the
+    `primary_ref` accessor (source of truth post-migration)."""
+    ref = getattr(obj, "primary_ref", None)
+    if ref is None:
+        return None
+    return ref.uri or None
+
+
+def list_entities(model: Model, subject_area: str | None = None) -> list[dict]:
+    """Grounding: every logical entity as {name, definition, attribute_count,
+    subject_area}, sorted by name. With `subject_area` (name or ULID), scope to
+    entities homed in / realising that area."""
+    scope: set[str] | None = None
+    if subject_area:
+        sa_id = _resolve_subject_area_id(model, subject_area)
+        # An unknown area name scopes to NOTHING (empty set), not to everything —
+        # returning the whole model on a typo'd filter would silently mislead.
+        if sa_id is None:
+            return []
+        sa = model.subject_areas.get(sa_id)
+        ce_ids = set(sa.members) if sa else set()
+        ce_ids |= {
+            ce.id for ce in model.conceptual_entities.values() if ce.subject_area == sa_id
+        }
+        scope = {le.id for le in model.logical_entities.values() if le.realises in ce_ids}
+
+    out: list[dict] = []
+    for le in sorted(model.logical_entities.values(), key=lambda e: e.name):
+        if scope is not None and le.id not in scope:
+            continue
+        ce = model.conceptual_entities.get(le.realises) if le.realises else None
+        sa = (
+            model.subject_areas.get(ce.subject_area)
+            if ce and ce.subject_area
+            else None
+        )
+        out.append(
+            {
+                "name": le.name,
+                "definition": (le.definition or (ce.definition if ce else None)),
+                "attribute_count": len(le.attributes),
+                "subject_area": sa.name if sa else None,
+            }
+        )
+    return out
+
+
+def _keys_for(model: Model, le: LogicalEntity) -> list[dict]:
+    """The entity's key groups (erwin Key Groups) — pk / unique / alternate / index —
+    with member attribute names in key order. This is what lets a caller reason about
+    normalization (candidate keys, functional dependencies) and identity.
+
+    When no explicit `pk` KeyGroup is present, fall back to the legacy per-attribute
+    `role: business_key` convention (which the IR documents as still supported): the
+    business-key attributes form an inferred primary key, flagged `inferred: True` so
+    a caller knows it came from the convention, not a declared KeyGroup."""
+    attr_name = {a.id: a.name for a in le.attributes}
+    keys: list[dict] = []
+    for kg in sorted(model.key_groups.values(), key=lambda k: (k.type, k.name)):
+        if kg.entity != le.id:
+            continue
+        keys.append(
+            {
+                "name": kg.name,
+                "type": kg.type,
+                "columns": [attr_name.get(m, m) for m in kg.members],
+            }
+        )
+    # Legacy fallback: no declared pk KeyGroup, but business_key attributes exist.
+    if not any(k["type"] == "pk" for k in keys):
+        bk = [a.name for a in le.attributes if a.role == "business_key"]
+        if bk:
+            keys.insert(0, {"name": "pk (from business_key)", "type": "pk",
+                            "columns": bk, "inferred": True})
+    return keys
+
+
+def get_entity(model: Model, name: str) -> dict | None:
+    """Full detail for one entity by name or ULID: attributes (with types + ontology
+    alignment), key groups (pk/unique/alternate), the conceptual layer, and the
+    relationships it participates in. Returns None when no entity matches."""
+    le = _entity_by_name(model, name)
+    if le is None:
+        return None
+    ce = model.conceptual_entities.get(le.realises) if le.realises else None
+
+    attributes = [
+        {
+            "name": a.name,
+            "definition": a.definition,
+            "domain": a.domain,
+            "nullable": a.nullable,
+            "ontology": _ontology_summary(a),
+        }
+        for a in le.attributes
+    ]
+    keys = _keys_for(model, le)
+
+    rels: list[dict] = []
+    for r in sorted(model.relationships.values(), key=lambda x: x.name):
+        from_e = model.logical_entities.get(r.from_.entity)
+        to_e = model.logical_entities.get(r.to.entity)
+        if le.id not in (r.from_.entity, r.to.entity):
+            continue
+        rels.append(
+            {
+                "name": r.name,
+                "from": from_e.name if from_e else r.from_.entity,
+                "to": to_e.name if to_e else r.to.entity,
+                "definition": r.definition,
+            }
+        )
+
+    return {
+        "name": le.name,
+        "definition": le.definition or (ce.definition if ce else None),
+        "pattern": le.pattern,
+        "unmanaged": bool(le.unmanaged),
+        "conceptual": (
+            {
+                "name": ce.name,
+                "definition": ce.definition,
+                "synonyms": list(ce.synonyms),
+                "ontology": _ontology_summary(ce),
+                "ontology_layer": ce.ontology_layer,
+            }
+            if ce
+            else None
+        ),
+        "keys": keys,
+        "attributes": attributes,
+        "relationships": rels,
+    }
+
+
+def get_model_context(model: Model) -> dict:
+    """A condensed model summary sized for a chat context window: the project name,
+    counts, subject areas with their entity lists, and every relationship as a
+    from->to sentence. Enough to ground an answer without shipping the whole model."""
+    ce_by_id = model.conceptual_entities
+
+    # subject areas -> the logical entities homed there
+    areas: list[dict] = []
+    for sa in sorted(model.subject_areas.values(), key=lambda s: s.name):
+        ce_ids = set(sa.members)
+        ce_ids |= {ce.id for ce in ce_by_id.values() if ce.subject_area == sa.id}
+        names = sorted(
+            le.name for le in model.logical_entities.values() if le.realises in ce_ids
+        )
+        areas.append({"name": sa.name, "definition": sa.definition, "entities": names})
+
+    unhomed = sorted(
+        le.name
+        for le in model.logical_entities.values()
+        if not le.realises or ce_by_id.get(le.realises) is None
+    )
+
+    relationships = []
+    for r in sorted(model.relationships.values(), key=lambda x: x.name):
+        from_e = model.logical_entities.get(r.from_.entity)
+        to_e = model.logical_entities.get(r.to.entity)
+        relationships.append(
+            {
+                "name": r.name,
+                "from": from_e.name if from_e else r.from_.entity,
+                "to": to_e.name if to_e else r.to.entity,
+            }
+        )
+
+    return {
+        "project": model.config.name,
+        "counts": {
+            "entities": len(model.logical_entities),
+            "relationships": len(model.relationships),
+            "subject_areas": len(model.subject_areas),
+            "terms": len(model.terms),
+        },
+        "subject_areas": areas,
+        "unassigned_entities": unhomed,
+        "relationships": relationships,
+    }
+
+
+def entities_detail(model: Model, limit: int = 40) -> dict:
+    """Compact detail for EVERY entity — attributes (name:domain, nullability), key
+    groups, and relationship names — for grounding a model-wide question ("are all
+    entities in BCNF?", "which lack a primary key?"). Deliberately terse: a real
+    model can have 150+ entities, so each is one small record, not the full nested
+    `get_entity` shape, keeping the whole thing within a chat context window.
+
+    Capped at `limit` entities (alphabetical). `truncated` is True when the model has
+    more, with `total` and `shown` so the caller can tell the user the answer covers
+    only the first N and to narrow the question."""
+    all_le = sorted(model.logical_entities.values(), key=lambda e: e.name)
+    shown = all_le[:limit]
+    ce_by_id = model.conceptual_entities
+
+    # relationship names per entity, one pass
+    rels_by_entity: dict[str, list[str]] = {}
+    for r in model.relationships.values():
+        from_e = model.logical_entities.get(r.from_.entity)
+        to_e = model.logical_entities.get(r.to.entity)
+        label = f"{from_e.name if from_e else '?'}→{to_e.name if to_e else '?'}"
+        for end in (r.from_.entity, r.to.entity):
+            rels_by_entity.setdefault(end, []).append(label)
+
+    records = []
+    for le in shown:
+        ce = ce_by_id.get(le.realises) if le.realises else None
+        records.append(
+            {
+                "name": le.name,
+                # "col:domain" (+ "?" when nullable) — enough to reason about
+                # attributes and dependencies without the full attribute objects.
+                "attributes": [
+                    f"{a.name}:{a.domain or '?'}{'?' if a.nullable else ''}"
+                    for a in le.attributes
+                ],
+                "keys": [
+                    {"type": kg["type"], "columns": kg["columns"]}
+                    for kg in _keys_for(model, le)
+                ],
+                "relationships": sorted(set(rels_by_entity.get(le.id, []))),
+                "ontology": _ontology_summary(ce) if ce else None,
+            }
+        )
+
+    return {
+        "project": model.config.name,
+        "total": len(all_le),
+        "shown": len(shown),
+        "truncated": len(all_le) > limit,
+        "entities": records,
+    }
+
+
+def _resolve_subject_area_id(model: Model, ref: str) -> str | None:
+    """Accept a subject-area name or ULID; return its ULID, or None if unknown."""
+    if ref in model.subject_areas:
+        return ref
+    lref = ref.lower()
+    for sa in model.subject_areas.values():
+        if sa.name.lower() == lref:
+            return sa.id
+    return None
diff --git a/packages/mcp/pyproject.toml b/packages/mcp/pyproject.toml
new file mode 100644
index 0000000..a6d8e26
--- /dev/null
+++ b/packages/mcp/pyproject.toml
@@ -0,0 +1,25 @@
+[project]
+name = "modelith-mcp"
+version = "0.1.0"
+description = "Modelith MCP server — model + ontology tools for AI agents (Copilot Chat agent mode, Claude, etc.)"
+requires-python = ">=3.11"
+license = { text = "Apache-2.0" }
+dependencies = [
+    "modelith-core",
+    "modelith-ontology",
+    # v1 FastMCP API. mcp 2.x renamed FastMCP -> MCPServer and is still very new;
+    # editor MCP clients (VS Code, Claude Desktop, Cursor) target the v1 stdio
+    # server today, so pin below 2 until the ecosystem moves.
+    "mcp>=1.2,<2",
+]
+
+[build-system]
+requires = ["hatchling"]
+build-backend = "hatchling.build"
+
+[tool.hatch.build.targets.wheel]
+packages = ["src/mdl_mcp"]
+
+[tool.uv.sources]
+modelith-core = { workspace = true }
+modelith-ontology = { workspace = true }
diff --git a/packages/mcp/src/mdl_mcp/__init__.py b/packages/mcp/src/mdl_mcp/__init__.py
new file mode 100644
index 0000000..34079a2
--- /dev/null
+++ b/packages/mcp/src/mdl_mcp/__init__.py
@@ -0,0 +1,11 @@
+"""Modelith MCP server.
+
+Exposes the model as MCP tools for an AI agent (VS Code Copilot Chat in *agent*
+mode, Claude Desktop, Cursor, …). Reads come from `mdl_core.query`; writes go
+through `mdl_core.commands.apply_command`, so they take the same validation and
+concurrency path as every other Modelith write. Run it with `mdl mcp --repo <dir>`.
+"""
+
+from mdl_mcp.server import build_server, run
+
+__all__ = ["build_server", "run"]
diff --git a/packages/mcp/src/mdl_mcp/server.py b/packages/mcp/src/mdl_mcp/server.py
new file mode 100644
index 0000000..737783e
--- /dev/null
+++ b/packages/mcp/src/mdl_mcp/server.py
@@ -0,0 +1,245 @@
+"""The Modelith MCP server (stdio).
+
+Seven tools, mirroring the AI spec. Reads (`list_entities`, `get_entity`,
+`search_ontology`, `get_model_context`, `validate`) come from the shared query
+layer; writes (`create_entity`, `update_entity`) go through
+`mdl_core.commands.apply_command` so they are validated and fingerprint-guarded
+exactly like a canvas or CLI edit.
+
+Direct local write is intentional (spec §1): this runs against the engineer's own
+checkout, a different trust boundary than the SME app's propose-as-PR flow.
+
+The server is bound to ONE model directory, passed at startup (`--repo`). A
+programmatically registered MCP server has no implicit repo scope the way a
+committed `.vscode/mcp.json` would, so VS Code passes the workspace folder through.
+"""
+
+from __future__ import annotations
+
+import json
+from functools import lru_cache
+from pathlib import Path
+from typing import Any
+
+from mcp.server.fastmcp import FastMCP
+
+from mdl_core import query
+from mdl_core.commands import CommandError, StaleModelError, apply_command
+from mdl_core.diagnostics import Severity
+from mdl_core.repo import ModelRepo
+from mdl_core.validate import validate as run_validate
+
+
+def build_server(repo_dir: Path) -> FastMCP:
+    """Assemble the MCP server bound to `repo_dir`. Split out from `run()` so tests
+    can drive the tools without stdio."""
+    repo_dir = repo_dir.resolve()
+    mcp = FastMCP("modelith")
+
+    def _model():
+        # Reload per call: an agent's own writes (and a human editing alongside it)
+        # must be visible to the next read. Cheap on the demo; the LSP/server carry
+        # their own caches for the hot paths.
+        return ModelRepo.load(repo_dir).model
+
+    # --- reads ------------------------------------------------------------------
+
+    @mcp.tool()
+    def list_entities(subject_area: str | None = None) -> str:
+        """List the model's logical entities (name, definition, attribute count,
+        subject area). Grounding: call this first to see what already exists.
+        Optionally scope to a subject area by name or ULID."""
+        return _json(query.list_entities(_model(), subject_area))
+
+    @mcp.tool()
+    def get_entity(name: str) -> str:
+        """Full detail for one entity by name (or ULID): its attributes with types
+        and ontology alignment, its key groups (pk / unique / alternate), the
+        conceptual layer, and the relationships it takes part in. Returns an error
+        object if no entity matches."""
+        hit = query.get_entity(_model(), name)
+        if hit is None:
+            return _json({"error": f"no entity named {name!r}"})
+        return _json(hit)
+
+    @mcp.tool()
+    def get_model_context() -> str:
+        """A condensed summary of the whole model — project, counts, subject areas
+        with their entities, and every relationship — sized for a chat context
+        window. Use it to orient before answering a broad question."""
+        return _json(query.get_model_context(_model()))
+
+    @mcp.tool()
+    def search_ontology(text: str, within: str | None = None, limit: int = 10) -> str:
+        """Search the model's configured industry/enterprise vocabularies for terms
+        matching `text` (e.g. to find a FIBO class to align an entity to). `within`
+        scopes to a single vocabulary id (see the source's ontology list). Returns
+        ranked hits with their prefixed IRI, label and definition."""
+        reg = _registry(repo_dir)
+        if reg is None:
+            return _json({"warning": "no ontology sources configured", "results": []})
+        results = reg.search(text, within=within, limit=limit)
+        return _json(
+            {
+                "results": [
+                    {
+                        "prefixed": r.prefixed,
+                        "iri": r.iri,
+                        "label": r.label,
+                        "definition": r.definition,
+                        "source": r.source,
+                    }
+                    for r in results
+                ]
+            }
+        )
+
+    @mcp.tool()
+    def validate() -> str:
+        """Validate the model (schema, references, ontology rules, naming) and return
+        the diagnostics. Use this before writing, or to check the model's health."""
+        diags = run_validate(_model())
+        return _json(
+            {
+                "ok": not diags.has(Severity.error),
+                "diagnostics": [
+                    {"code": d.code, "severity": d.severity.value, "message": d.message}
+                    for d in diags.items
+                ],
+            }
+        )
+
+    # --- writes (validated + fingerprint-guarded via apply_command) -------------
+
+    @mcp.tool()
+    def create_entity(
+        name: str,
+        definition: str | None = None,
+        subject_area: str | None = None,
+        layer: str | None = None,
+    ) -> str:
+        """Create a conceptual + logical entity pair. Writes to the local model on
+        disk (validated before it lands). `subject_area` is a subject-area ULID;
+        `layer` is one of industry|core|domain|specialised. Returns the new entity's
+        ULID or an error object."""
+        return _write(
+            repo_dir,
+            "create_entity",
+            {
+                "name": name,
+                "definition": definition,
+                "subject_area": subject_area,
+                "layer": layer,
+            },
+        )
+
+    @mcp.tool()
+    def update_entity(name: str, changes: dict[str, Any]) -> str:
+        """Update an existing entity by name (or ULID). `changes` may contain:
+        `definition` (str), `rename` (str, the new name), `add_attribute`
+        ({name, domain?, definition?, nullable?}), or `subject_area` (ULID). Each is
+        applied through the validated command engine; the whole update is rejected
+        (nothing written) if any step is invalid. Returns ok or an error object."""
+        le = query._entity_by_name(_model(), name)  # noqa: SLF001 - shared helper
+        if le is None:
+            return _json({"error": f"no entity named {name!r}"})
+        return _apply_updates(repo_dir, le.id, le.realises, changes)
+
+    return mcp
+
+
+# --- helpers -------------------------------------------------------------------
+
+
+def _apply_updates(
+    repo_dir: Path, entity_id: str, conceptual_id: str | None, changes: dict[str, Any]
+) -> str:
+    """Map the `update_entity` change bag onto field-scoped commands. Applied in a
+    fixed order; each goes through apply_command, so validation runs after every
+    step and a bad change stops the sequence. Definition and subject area live on the
+    CONCEPTUAL node, so those ops take the conceptual ULID; rename and attributes are
+    logical."""
+    applied: list[str] = []
+    try:
+        if "definition" in changes:
+            if conceptual_id is None:
+                return _json({"error": "entity has no conceptual layer to set a definition on"})
+            apply_command(
+                repo_dir,
+                "set_definition",
+                {"id": conceptual_id, "definition": changes["definition"]},
+            )
+            applied.append("definition")
+        if "rename" in changes:
+            apply_command(repo_dir, "rename_entity", {"id": entity_id, "name": changes["rename"]})
+            applied.append("rename")
+        if "subject_area" in changes:
+            if conceptual_id is None:
+                return _json({"error": "entity has no conceptual layer to home in a subject area"})
+            apply_command(
+                repo_dir,
+                "set_subject_area",
+                {"id": conceptual_id, "subject_area": changes["subject_area"]},
+            )
+            applied.append("subject_area")
+        if "add_attribute" in changes:
+            attr = changes["add_attribute"]
+            if not isinstance(attr, dict) or "name" not in attr:
+                return _json({"error": "add_attribute must be an object with at least a name"})
+            apply_command(repo_dir, "add_attribute", {"entity_id": entity_id, **attr})
+            applied.append(f"add_attribute:{attr['name']}")
+    except StaleModelError as e:
+        return _json({"error": f"model changed on disk mid-update: {e}", "applied": applied})
+    except (CommandError, FileNotFoundError, ValueError) as e:
+        return _json({"error": str(e), "applied": applied})
+    if not applied:
+        return _json({"error": "no recognised keys in changes", "applied": []})
+    return _json({"ok": True, "applied": applied})
+
+
+def _write(repo_dir: Path, op: str, payload: dict) -> str:
+    """Run one write command and shape the result (created id + diagnostics), or an
+    error object the agent can read."""
+    try:
+        result = apply_command(repo_dir, op, {k: v for k, v in payload.items() if v is not None})
+    except StaleModelError as e:
+        return _json({"error": f"model changed on disk: {e}"})
+    except (CommandError, FileNotFoundError, ValueError) as e:
+        return _json({"error": str(e)})
+    return _json(
+        {
+            "ok": result.ok,
+            "created_id": result.created_id,
+            # CommandResult.diagnostics is already a list of {code, severity, message}
+            # dicts (validated on the reload apply_command does after each write).
+            "diagnostics": result.diagnostics,
+            "error": result.error,
+        }
+    )
+
+
+@lru_cache(maxsize=8)
+def _registry(repo_dir: Path):
+    """Build + load the ontology registry once per repo dir. Returns None when the
+    model has no ontology stack configured (search then returns an empty result)."""
+    try:
+        from mdl_ontology import build_registry
+
+        model = ModelRepo.load(repo_dir).model
+        stack = getattr(model.config, "ontology_stack", None)
+        if not stack:
+            return None
+        reg = build_registry(repo_dir, stack)
+        reg.load()
+        return reg
+    except Exception:  # noqa: BLE001 - a bad ontology config must not crash the server
+        return None
+
+
+def _json(obj) -> str:
+    return json.dumps(obj, indent=2, default=str)
+
+
+def run(repo_dir: Path) -> None:
+    """Start the server on stdio (the transport VS Code / Claude Desktop use)."""
+    build_server(repo_dir).run()
diff --git a/packages/mcp/tests/conftest.py b/packages/mcp/tests/conftest.py
new file mode 100644
index 0000000..08dec24
--- /dev/null
+++ b/packages/mcp/tests/conftest.py
@@ -0,0 +1,15 @@
+import sys
+from pathlib import Path
+
+import pytest
+
+_CORE_TESTS = Path(__file__).resolve().parents[2] / "core" / "tests"
+sys.path.insert(0, str(_CORE_TESTS))
+
+from model_builders import write_model  # noqa: E402
+
+
+@pytest.fixture
+def model_dir(tmp_path):
+    write_model(tmp_path)
+    return tmp_path
diff --git a/packages/mcp/tests/test_query.py b/packages/mcp/tests/test_query.py
new file mode 100644
index 0000000..aff123b
--- /dev/null
+++ b/packages/mcp/tests/test_query.py
@@ -0,0 +1,86 @@
+"""The shared query layer (mdl_core.query) that both AI frontends read from."""
+
+from __future__ import annotations
+
+from mdl_core.query import entities_detail, get_entity, get_model_context, list_entities
+from mdl_core.repo import ModelRepo
+
+
+def _model(model_dir):
+    return ModelRepo.load(model_dir).model
+
+
+def test_list_entities_names_and_counts(model_dir):
+    ents = list_entities(_model(model_dir))
+    by_name = {e["name"]: e for e in ents}
+    assert {"counterparty", "trade"} <= set(by_name)
+    # counterparty has two attributes in the builder
+    assert by_name["counterparty"]["attribute_count"] == 2
+    assert by_name["counterparty"]["subject_area"] == "Trading"
+
+
+def test_list_entities_scoped_to_subject_area(model_dir):
+    # Both entities are homed in Trading; an unknown area scopes to nothing.
+    assert list_entities(_model(model_dir), subject_area="Trading")
+    assert list_entities(_model(model_dir), subject_area="no-such-area") == []
+
+
+def test_get_entity_by_name_carries_relationships(model_dir):
+    e = get_entity(_model(model_dir), "counterparty")
+    assert e is not None
+    assert e["name"] == "counterparty"
+    assert e["conceptual"]["name"] == "Counterparty"
+    # the trade_has_counterparty relationship touches counterparty
+    assert any(r["name"] == "trade_has_counterparty" for r in e["relationships"])
+    assert {a["name"] for a in e["attributes"]} == {"counterparty_id", "legal_name"}
+
+
+def test_get_entity_unknown_returns_none(model_dir):
+    assert get_entity(_model(model_dir), "does_not_exist") is None
+
+
+def test_get_entity_accepts_a_ulid(model_dir):
+    model = _model(model_dir)
+    le = next(e for e in model.logical_entities.values() if e.name == "trade")
+    assert get_entity(model, le.id)["name"] == "trade"
+
+
+def test_get_entity_infers_pk_from_business_key(model_dir):
+    # The builder uses the legacy role:business_key convention (no KeyGroup objects).
+    # _keys_for should synthesise an inferred pk from it, so normalization questions
+    # can reason about the key even without a declared KeyGroup.
+    e = get_entity(_model(model_dir), "counterparty")
+    assert isinstance(e["keys"], list)
+    pk = next((k for k in e["keys"] if k["type"] == "pk"), None)
+    assert pk is not None
+    assert pk["inferred"] is True
+    assert pk["columns"] == ["counterparty_id"]
+
+
+def test_entities_detail_is_compact_and_complete(model_dir):
+    d = entities_detail(_model(model_dir))
+    assert d["total"] == 2
+    assert d["truncated"] is False
+    names = {e["name"] for e in d["entities"]}
+    assert {"counterparty", "trade"} == names
+    cp = next(e for e in d["entities"] if e["name"] == "counterparty")
+    # attributes are compact "name:domain[?]" strings
+    assert any(a.startswith("counterparty_id:") for a in cp["attributes"])
+    assert isinstance(cp["keys"], list)
+
+
+def test_entities_detail_truncates_at_limit(model_dir):
+    d = entities_detail(_model(model_dir), limit=1)
+    assert d["total"] == 2
+    assert d["shown"] == 1
+    assert d["truncated"] is True
+    assert len(d["entities"]) == 1
+
+
+def test_get_model_context_summary(model_dir):
+    ctx = get_model_context(_model(model_dir))
+    assert ctx["project"] == "testmodel"
+    assert ctx["counts"]["entities"] == 2
+    assert ctx["counts"]["relationships"] == 1
+    assert any(a["name"] == "Trading" for a in ctx["subject_areas"])
+    assert any(r["name"] == "trade_has_counterparty" for r in ctx["relationships"])
diff --git a/packages/mcp/tests/test_server.py b/packages/mcp/tests/test_server.py
new file mode 100644
index 0000000..aeb2557
--- /dev/null
+++ b/packages/mcp/tests/test_server.py
@@ -0,0 +1,96 @@
+"""The MCP server tools: reads over the query layer, writes through apply_command.
+
+The FastMCP tool-call API is async; we drive it with a small asyncio.run helper so
+the suite needs no async-test plugin (the project has none, and adding one is a
+separate decision)."""
+
+from __future__ import annotations
+
+import asyncio
+import json
+
+from mdl_mcp.server import build_server
+
+
+def _call(srv, name, args):
+    """Invoke a FastMCP tool synchronously and return its parsed JSON result."""
+
+    async def _run():
+        res = await srv.call_tool(name, args)
+        content = res[0] if isinstance(res, tuple) else res
+        return content[0].text if content and hasattr(content[0], "text") else str(content)
+
+    return json.loads(asyncio.run(_run()))
+
+
+def _tool_names(srv):
+    return {t.name for t in asyncio.run(srv.list_tools())}
+
+
+def test_tools_are_registered(model_dir):
+    names = _tool_names(build_server(model_dir))
+    assert {
+        "list_entities",
+        "get_entity",
+        "get_model_context",
+        "search_ontology",
+        "validate",
+        "create_entity",
+        "update_entity",
+    } <= names
+
+
+def test_get_entity_tool_reads(model_dir):
+    srv = build_server(model_dir)
+    e = _call(srv, "get_entity", {"name": "counterparty"})
+    assert e["conceptual"]["name"] == "Counterparty"
+
+
+def test_get_entity_tool_unknown_is_error_object(model_dir):
+    srv = build_server(model_dir)
+    e = _call(srv, "get_entity", {"name": "nope"})
+    assert "error" in e
+
+
+def test_create_entity_writes_to_disk(model_dir):
+    srv = build_server(model_dir)
+    r = _call(srv, "create_entity", {"name": "region", "definition": "A geographic region."})
+    assert r["ok"] is True
+    assert r["created_id"]
+    # visible to the very next read (the server reloads per call)
+    e = _call(srv, "get_entity", {"name": "region"})
+    assert e["name"] == "region"
+    assert (model_dir / "logical" / "entities" / "region.yaml").exists()
+
+
+def test_update_entity_add_attribute_and_definition(model_dir):
+    srv = build_server(model_dir)
+    _call(srv, "create_entity", {"name": "region"})
+    r = _call(
+        srv,
+        "update_entity",
+        {
+            "name": "region",
+            "changes": {
+                "definition": "A named geographic area.",
+                "add_attribute": {"name": "region_code", "domain": "string", "nullable": False},
+            },
+        },
+    )
+    assert r["ok"] is True
+    assert "definition" in r["applied"]
+    e = _call(srv, "get_entity", {"name": "region"})
+    assert e["definition"] == "A named geographic area."
+    assert any(a["name"] == "region_code" for a in e["attributes"])
+
+
+def test_update_entity_unknown_is_error(model_dir):
+    srv = build_server(model_dir)
+    r = _call(srv, "update_entity", {"name": "ghost", "changes": {"definition": "x"}})
+    assert "error" in r
+
+
+def test_validate_tool_reports_ok(model_dir):
+    srv = build_server(model_dir)
+    r = _call(srv, "validate", {})
+    assert r["ok"] is True
diff --git a/pyproject.toml b/pyproject.toml
index e3370ff..8c2b13f 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -27,6 +27,7 @@ dependencies = [
     "python-multipart>=0.0.18",
     "pygls>=2.0",
     "sqlglot>=25",
+    "mcp>=1.2,<2",
 ]
 
 # Optional OpenTelemetry export for the audit log (spec §20). `pip install
@@ -70,6 +71,7 @@ dev-mode-dirs = [
     "packages/catalog/src",
     "packages/server/src",
     "packages/lsp/src",
+    "packages/mcp/src",
     "packages/adapters/collibra/src",
 ]
 
diff --git a/uv.lock b/uv.lock
index e03d5a3..634d690 100644
--- a/uv.lock
+++ b/uv.lock
@@ -2,7 +2,8 @@ version = 1
 revision = 1
 requires-python = ">=3.11"
 resolution-markers = [
-    "python_full_version >= '3.12'",
+    "python_full_version >= '3.14'",
+    "python_full_version >= '3.12' and python_full_version < '3.14'",
     "python_full_version < '3.12'",
 ]
 
@@ -21,6 +22,7 @@ members = [
     "modelith-emit-semantic",
     "modelith-governance",
     "modelith-lsp",
+    "modelith-mcp",
     "modelith-ontology",
     "modelith-reverse",
     "modelith-server",
@@ -88,6 +90,104 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/0b/a7/71ac2cff56fec219ed242bb11b8efb69fcc4bec75db06fb7bfe35de520e6/certifi-2026.7.22-py3-none-any.whl", hash = "sha256:62f22742b58a1a33014a2b6b706588a8d7e2a88ae7bd1a6ebe8c992928483775", size = 136983 },
 ]
 
+[[package]]
+name = "cffi"
+version = "2.1.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "pycparser", marker = "implementation_name != 'PyPy'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/9e/ef/008a1939e372c06329a3fce4279c02f328488f3526744906eeec3da7ad5f/cffi-2.1.1.tar.gz", hash = "sha256:dd31f52ea1086513bb9df30f8fcee9b8918323ae067a3d5b78bc826a000712be", size = 530807 }
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+    { url = "https://files.pythonhosted.org/packages/4e/98/af9b3db77d47fcbe6c8c1f36e2c2147ec70292819e99c325f871584a1c11/rpds_py-2026.6.3-pp311-pypy311_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a3450b693fde92133e9f51060568a4c31fcca76d5e53bbd611e689ca446517e9", size = 380059 },
+    { url = "https://files.pythonhosted.org/packages/c9/ba/0efd8668b97c1d26a61566386c636a7a7a09829e474fdf807caa15a2c844/rpds_py-2026.6.3-pp311-pypy311_pp73-manylinux_2_31_riscv64.whl", hash = "sha256:5e8d07bddee435a2ff6f1920e18feff28d0bc4533e42f4bf6927fbd073312c41", size = 393235 },
+    { url = "https://files.pythonhosted.org/packages/62/90/8c139ee9690f73b0829f32647de6f40d826f8f443af6fa72644f96351aac/rpds_py-2026.6.3-pp311-pypy311_pp73-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:3a83ae6c67b7676b9878378547ca8e93ed77a580037bcbcd1d32f739e1e6089c", size = 413008 },
+    { url = "https://files.pythonhosted.org/packages/9c/97/0043896fdd7828ce09a1d9a8b06433714d0960fc4ff3fc4aa72b666b764e/rpds_py-2026.6.3-pp311-pypy311_pp73-musllinux_1_2_aarch64.whl", hash = "sha256:2bfd04c19ddbd6640de0b51894d764bd2758854d5b75bd102d2ef10cb9c293a9", size = 558118 },
+    { url = "https://files.pythonhosted.org/packages/f6/40/02355f0e134f783a8f9814c4680a1bd311d37671577a5964ea838573ff37/rpds_py-2026.6.3-pp311-pypy311_pp73-musllinux_1_2_i686.whl", hash = "sha256:ca6546b66be9dc4738b1b043d5ebd5488c66c578c5ff0fd0e8065313fe3afb76", size = 623138 },
+    { url = "https://files.pythonhosted.org/packages/10/85/48f0abdcef5cce4e034c7a5b0ceeceba0b01bf0d942824f4bb720afe2dec/rpds_py-2026.6.3-pp311-pypy311_pp73-musllinux_1_2_x86_64.whl", hash = "sha256:8e65860d238379ed982fd9ba690579b5e95af2f4840f99c772816dbe573cb826", size = 586486 },
+]
+
 [[package]]
 name = "ruamel-yaml"
 version = "0.19.1"
@@ -1416,6 +1859,19 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/08/9f/3dd2ec8dd84a33e0092f1c815267bc055073f9e833b93e389c59c10f62cc/sqlglot-30.18.0-py3-none-any.whl", hash = "sha256:ee0f9a9f3e2193e763c326e52dfb377b96fdb4292f6305c3ff2d9a220e71c601", size = 748788 },
 ]
 
+[[package]]
+name = "sse-starlette"
+version = "3.4.11"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "anyio" },
+    { name = "starlette" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/2b/54/6767bb789b2f2fed6e0f953df949cd39dc263a384c1b65a95232598621d6/sse_starlette-3.4.11.tar.gz", hash = "sha256:1bae716c02f3e6f294be41ff333220692dae7c3cbab077c900f159676719dade", size = 34972 }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/98/6a/2ba3ed4a69babf3afdddf7d8314a48d87562c0a442206bbc2a1b50d5efc0/sse_starlette-3.4.11-py3-none-any.whl", hash = "sha256:c7b2244bdff016fe7f64e10075e89a3e6bbf899649cc89b0fe884b5545042453", size = 17122 },
+]
+
 [[package]]
 name = "starlette"
 version = "1.3.1"
diff --git a/vscode/CHANGELOG.md b/vscode/CHANGELOG.md
index c45af60..6de9037 100644
--- a/vscode/CHANGELOG.md
+++ b/vscode/CHANGELOG.md
@@ -1,5 +1,15 @@
 # Changelog
 
+## Unreleased
+
+- **AI assistance.** Two Copilot Chat integrations backed by the same `mdl` engine:
+  a bundled MCP server (`mdl mcp`) exposing model + ontology tools in Agent mode
+  (`list_entities`, `get_entity`, `search_ontology`, `get_model_context`, `validate`,
+  `create_entity`, `update_entity`), and an `@modelith` chat participant for Ask mode
+  (`/list`, `/explain <entity>`). Registered automatically; no `mcp.json` to write.
+- **Import.** *Modelith: Import to Model* — right-click a `.sql`/`.mmd` file (or the
+  command palette) to open the Import wizard on the embedded canvas.
+
 ## 0.1.0
 
 First release.
diff --git a/vscode/LICENSE b/vscode/LICENSE
new file mode 100644
index 0000000..d645695
--- /dev/null
+++ b/vscode/LICENSE
@@ -0,0 +1,202 @@
+
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+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
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+          of the NOTICE file are for informational purposes only and
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+          notices within Derivative Works that You distribute, alongside
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+      the conditions stated in this License.
+
+   5. Submission of Contributions. Unless You explicitly state otherwise,
+      any Contribution intentionally submitted for inclusion in the Work
+      by You to the Licensor shall be under the terms and conditions of
+      this License, without any additional terms or conditions.
+      Notwithstanding the above, nothing herein shall supersede or modify
+      the terms of any separate license agreement you may have executed
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+
+   6. Trademarks. This License does not grant permission to use the trade
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+
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+   you may not use this file except in compliance with the License.
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+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
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diff --git a/vscode/README.md b/vscode/README.md
index 7fcbde2..06fbb56 100644
--- a/vscode/README.md
+++ b/vscode/README.md
@@ -43,6 +43,27 @@ embeds the live canvas, so whatever the CLI understands, the editor shows.
   locations. A standard `uv tool install modelith-dbt` needs no configuration. If nothing
   matches, run `which mdl` in the integrated terminal and set that as `modelith.mdlPath`.
 
+## AI assistance (Copilot Chat)
+
+Modelith registers two AI integrations, one per Copilot Chat mode. Both use the same
+`mdl` engine already detected for everything else — no `mcp.json` to write, no separate
+install.
+
+- **Agent mode — Modelith MCP tools.** In Copilot Chat's *Agent* mode the model is available
+  as tools: `list_entities`, `get_entity`, `search_ontology`, `get_model_context`, `validate`,
+  and the write tools `create_entity` / `update_entity`. The agent can ground itself in what
+  already exists, search the ontology for an alignment, and write validated entities straight
+  into your checkout (direct-write — you review the git diff, the same trust boundary as the
+  CLI). The tools **only run in Agent mode**: tool invocation needs the plan/act/observe loop
+  that Ask mode does not have. The server is the `mdl mcp` subcommand, registered automatically
+  on activation and scoped to your workspace model. Requires VS Code 1.99+ (older hosts keep
+  every other feature; only the tools are absent).
+- **Ask mode — `@modelith`.** In *Ask* mode, where MCP tools cannot run, type `@modelith` and
+  ask about the model: name an entity for its attributes / ontology alignment / relationships,
+  `@modelith /list` for the entity list, `@modelith /explain <entity>` for one in full, or a
+  general question for a model summary. It reads the model through the same query layer the MCP
+  tools use, so the two modes answer consistently.
+
 ## Devcontainers
 
 The extension declares `"extensionKind": ["workspace"]`, so in a devcontainer it runs inside
diff --git a/vscode/package.json b/vscode/package.json
index 3ca6ab9..1064b9f 100644
--- a/vscode/package.json
+++ b/vscode/package.json
@@ -30,7 +30,8 @@
     "theme": "dark"
   },
   "engines": {
-    "vscode": "^1.85.0"
+    "vscode": "^1.85.0",
+    "node": ">=18"
   },
   "categories": [
     "Data Science",
@@ -42,9 +43,34 @@
     "workspace"
   ],
   "activationEvents": [
-    "workspaceContains:**/mdl-project.yaml"
+    "workspaceContains:**/mdl-project.yaml",
+    "onChatParticipant:modelith.chat"
   ],
   "contributes": {
+    "mcpServerDefinitionProviders": [
+      {
+        "id": "modelith.mcp",
+        "label": "Modelith"
+      }
+    ],
+    "chatParticipants": [
+      {
+        "id": "modelith.chat",
+        "name": "modelith",
+        "fullName": "Modelith",
+        "description": "Ask about entities, relationships, and ontology alignment in this model.",
+        "commands": [
+          {
+            "name": "explain",
+            "description": "Explain one entity: its attributes, ontology alignment, and relationships."
+          },
+          {
+            "name": "list",
+            "description": "List the model's entities."
+          }
+        ]
+      }
+    ],
     "commands": [
       {
         "command": "modelith.validate",
@@ -191,12 +217,13 @@
     }
   },
   "scripts": {
+    "check-node": "node -e \"if(+process.versions.node.split('.')[0]<18){console.error('\\n  Node '+process.versions.node+' is too old for the build toolchain (vsce/esbuild need Node 18+).\\n  Use Node 20:  nvm use 20   (or: export NVM_DIR=$HOME/.nvm; . $NVM_DIR/nvm.sh; nvm use 20)\\n');process.exit(1)}\"",
     "vscode:prepublish": "npm run build -- --minify",
-    "build": "tsc --noEmit && node esbuild.mjs",
+    "build": "npm run check-node && tsc --noEmit && node esbuild.mjs",
     "watch": "node esbuild.mjs --watch",
     "typecheck": "tsc --noEmit",
-    "package": "npx @vscode/vsce package --no-dependencies",
-    "publish": "npx @vscode/vsce publish --no-dependencies"
+    "package": "npm run check-node && npx @vscode/vsce package --no-dependencies",
+    "publish": "npm run check-node && npx @vscode/vsce publish --no-dependencies"
   },
   "devDependencies": {
     "@types/node": "^20.11.0",
diff --git a/vscode/src/chatParticipant.ts b/vscode/src/chatParticipant.ts
new file mode 100644
index 0000000..4639b91
--- /dev/null
+++ b/vscode/src/chatParticipant.ts
@@ -0,0 +1,326 @@
+import * as vscode from "vscode";
+import { findMdl, findModelDir, runMdl } from "./mdl";
+
+/** The `@modelith` chat participant, for Copilot Chat's ASK mode — where MCP tools
+ * structurally cannot run (no agentic loop to invoke them from). It answers about
+ * the model itself: entities, relationships, ontology alignment.
+ *
+ * Intent handling is LLM-backed. The chat-participant API hands us `request.model`
+ * — the user's own already-authenticated Copilot model — so for free-text asks we
+ * GROUND with the real model facts (fetched through the same `mdl model` query
+ * layer the MCP server uses) and let that model compose the answer, pinned to the
+ * facts so it can't invent entities. Exact `/list` and `/explain <name>` keep a
+ * fast deterministic template; anything else — a question, a typo'd name, a
+ * multi-part ask — goes through the model. If no model is available (rare in
+ * Copilot Chat), a heuristic name match is the fallback, so the participant still
+ * works. One query layer in `packages/core`, two frontends, no drift. */
+
+interface ModelContext {
+  project: string;
+  counts: { entities: number; relationships: number; subject_areas: number; terms: number };
+  subject_areas: { name: string; definition: string | null; entities: string[] }[];
+  unassigned_entities: string[];
+  relationships: { name: string; from: string; to: string }[];
+}
+
+interface EntityRow {
+  name: string;
+  definition: string | null;
+  attribute_count: number;
+  subject_area: string | null;
+}
+
+interface EntityDetail {
+  name: string;
+  definition: string | null;
+  pattern: string | null;
+  conceptual: { name: string; ontology: string | null; ontology_layer: string | null } | null;
+  keys: { name: string; type: string; columns: string[] }[];
+  attributes: { name: string; domain: string | null; nullable: boolean; ontology: string | null }[];
+  relationships: { name: string; from: string; to: string }[];
+}
+
+interface EntitiesDetail {
+  project: string;
+  total: number;
+  shown: number;
+  truncated: boolean;
+  entities: {
+    name: string;
+    attributes: string[];
+    keys: { type: string; columns: string[] }[];
+    relationships: string[];
+    ontology: string | null;
+  }[];
+}
+
+/** A model-WIDE question needs every entity's attributes + keys, not just named
+ * ones — normalization ("BCNF", "3NF", "normalis"), coverage ("which entities",
+ * "any entity", "all"/"every"/"each"), or simply a question that names no entity at
+ * all. Everything else is targeted and grounds on the mentioned entities. */
+function isModelWide(prompt: string, mentioned: string[]): boolean {
+  const p = prompt.toLowerCase();
+  if (/\b(bcnf|[1-6]nf|normal(is|iz)|denormal)/.test(p)) return true;
+  if (/\b(which|any|all|every|each|no)\b.*\b(entit|model|table|key|attribute)/.test(p)) return true;
+  return mentioned.length === 0;
+}
+
+function escapeRegExp(s: string): string {
+  return s.replace(/[.*+?^${}()|[\]\\]/g, "\\$&");
+}
+
+/** Run an `mdl model …` read and parse its JSON stdout. Throws with the CLI's own
+ * message on a non-zero exit so the handler can surface it. */
+async function readModel<T>(dir: string, args: string[]): Promise<T> {
+  const bin = await findMdl(dir);
+  const r = await runMdl(bin, [...args, "-m", dir], dir);
+  if (r.code !== 0) {
+    try {
+      const obj = JSON.parse(r.stdout);
+      if (obj?.error) throw new Error(obj.error);
+    } catch {
+      /* not a JSON error object — fall through */
+    }
+    // A too-old `mdl` (a stale global install predating these commands) doesn't
+    // know `mdl model …` and prints Typer's usage dump. Turn that into an
+    // actionable message instead of leaking the raw usage text to chat.
+    const blob = (r.stdout + r.stderr).toLowerCase();
+    if (blob.includes("no such command") || blob.includes("usage: mdl")) {
+      throw new Error(
+        "Your installed `mdl` is out of date — it doesn't have the `model` command " +
+          "these features need. Upgrade it (`uv tool install --force modelith-dbt`, or " +
+          "reinstall from your working tree), then reload VS Code.",
+      );
+    }
+    throw new Error(r.stderr.trim() || r.stdout.trim() || `mdl ${args.join(" ")} failed`);
+  }
+  return JSON.parse(r.stdout) as T;
+}
+
+export function registerChatParticipant(ctx: vscode.ExtensionContext): void {
+  // The chat API is stable from VS Code 1.90+, but guard so activation never
+  // crashes on an older host that lacks it (as the MCP guard does for 1.99+).
+  if (!vscode.chat?.createChatParticipant) return;
+
+  const handler: vscode.ChatRequestHandler = async (request, _context, stream, token) => {
+    const dir = await findModelDir();
+    if (!dir) {
+      stream.markdown(
+        "No Modelith model found in this workspace (looked for `mdl-project.yaml`). " +
+          "Set `modelith.modelDir` if it lives somewhere unusual.",
+      );
+      return;
+    }
+
+    try {
+      if (request.command === "list") {
+        await renderList(dir, stream);
+        return;
+      }
+
+      const prompt = request.prompt.trim();
+      const rows = await readModel<EntityRow[]>(dir, ["model", "entities"]);
+      const names = rows.map((r) => r.name);
+
+      // Fast path: the prompt (after an optional /explain) IS an exact entity name.
+      // Render the deterministic card without an LLM round-trip.
+      const exact = names.find((n) => n.toLowerCase() === prompt.toLowerCase());
+      if (exact) {
+        await renderEntity(dir, exact, stream);
+        return;
+      }
+
+      // Everything else — a question, a name buried in a sentence, a typo — is
+      // resolved by the user's Copilot model, grounded in the real model facts.
+      if (request.model) {
+        await answerWithModel(dir, request, rows, stream, token);
+        return;
+      }
+
+      // No language model available: fall back to a heuristic name match, then a
+      // structural summary, so the participant still does something useful.
+      const guessed = guessEntity(prompt, names);
+      if (guessed) {
+        await renderEntity(dir, guessed, stream);
+        return;
+      }
+      await renderSummary(dir, stream);
+    } catch (e) {
+      stream.markdown(`\n\n⚠️ ${e instanceof Error ? e.message : String(e)}`);
+    }
+  };
+
+  const participant = vscode.chat.createChatParticipant("modelith.chat", handler);
+  participant.iconPath = undefined;
+  ctx.subscriptions.push(participant);
+}
+
+/** Ground the user's Copilot model with the model's facts and stream its answer.
+ *
+ * We assemble a compact fact sheet — the entity roster, the condensed model
+ * context, and (when the question clearly concerns specific entities) their full
+ * detail — and instruct the model to answer ONLY from it. Deterministic data,
+ * natural-language reasoning: it handles "what connects to instrument?", "which
+ * entities have no ontology alignment?", and a fuzzy "explain the instrument
+ * entity, everything about it" alike. */
+async function answerWithModel(
+  dir: string,
+  request: vscode.ChatRequest,
+  rows: EntityRow[],
+  stream: vscode.ChatResponseStream,
+  token: vscode.CancellationToken,
+): Promise<void> {
+  const names = rows.map((r) => r.name);
+  const context = await readModel<ModelContext>(dir, ["model", "context"]);
+
+  // Two grounding strategies, chosen by scope. A model-wide question (normalization,
+  // "which entities…", "all/every…") needs every entity's attributes + keys; a
+  // targeted one needs only the named entities in full. We never dump the whole
+  // model at full fidelity — `mdl model detail` is compact and capped, so a 150-
+  // entity model stays inside the context window (with a truncation notice).
+  const mentioned = names.filter((n) =>
+    new RegExp(`\\b${escapeRegExp(n)}\\b`, "i").test(request.prompt),
+  );
+  let facts: string;
+  let truncationNote = "";
+  if (isModelWide(request.prompt, mentioned)) {
+    const detail = await readModel<EntitiesDetail>(dir, ["model", "detail", "--limit", "120"]);
+    if (detail.truncated) {
+      truncationNote =
+        `\n\n_(This model has ${detail.total} entities; I grounded on the first ` +
+        `${detail.shown}. For a complete answer, ask about a subject area or specific ` +
+        "entities.)_";
+    }
+    facts = JSON.stringify({ context, entities_detail: detail }, null, 2);
+  } else {
+    const details: EntityDetail[] = [];
+    for (const n of mentioned.slice(0, 6)) {
+      try {
+        details.push(await readModel<EntityDetail>(dir, ["model", "entity", n]));
+      } catch {
+        /* skip an entity that vanished between the list and the read */
+      }
+    }
+    facts = JSON.stringify({ entities: names, context, detail: details }, null, 2);
+  }
+
+  const system =
+    "You are Modelith, a data-modeling assistant answering about ONE specific model " +
+    "inside VS Code. Answer the user's question using ONLY the JSON facts provided — " +
+    "the model's entities, subject areas, relationships, attributes and keys. Attribute " +
+    "strings are `name:domain` with a trailing `?` for nullable. Keys list pk / unique / " +
+    "alternate key groups; a pk marked inferred came from the legacy business_key " +
+    "convention but is still the entity's primary key. For normalization questions: you " +
+    "can reason from candidate keys (e.g. an entity whose only key is a single attribute, " +
+    "with all other attributes dependent on it, is trivially in BCNF for key-determined " +
+    "dependencies), but the model does NOT capture arbitrary functional dependencies — so " +
+    "say plainly that a full BCNF/3NF proof needs FDs the model doesn't record, and give " +
+    "the key-based assessment you can. Do not invent entities, attributes, keys, or " +
+    "alignments that are not in the facts. Be concise, use Markdown, and prefer the " +
+    "model's own names verbatim. This is read-only: for editing, point the user to " +
+    "Copilot Agent mode where the Modelith tools run.";
+
+  const messages = [
+    vscode.LanguageModelChatMessage.User(`${system}\n\nMODEL FACTS:\n${facts}`),
+    vscode.LanguageModelChatMessage.User(request.prompt),
+  ];
+
+  try {
+    const res = await request.model.sendRequest(messages, {}, token);
+    for await (const chunk of res.text) stream.markdown(chunk);
+    if (truncationNote) stream.markdown(truncationNote);
+  } catch (e) {
+    // The model call can fail (consent not granted, quota, offline). Fall back to
+    // the heuristic so the user still gets an answer, not a dead end.
+    const guessed = guessEntity(request.prompt, names);
+    if (guessed) {
+      await renderEntity(dir, guessed, stream);
+      return;
+    }
+    await renderSummary(dir, stream);
+    stream.markdown(
+      `\n\n_(Couldn't reach a language model — ${e instanceof Error ? e.message : String(e)} — ` +
+        "so I answered from the model's structure directly.)_",
+    );
+  }
+}
+
+/** Heuristic entity pick for the no-LLM fallback: the longest entity name that
+ * appears as a word-ish substring of the prompt. Deliberately simple. */
+function guessEntity(prompt: string, names: string[]): string | undefined {
+  const p = prompt.toLowerCase();
+  return names
+    .filter((n) => p.includes(n.toLowerCase()))
+    .sort((a, b) => b.length - a.length)[0];
+}
+
+async function renderEntity(
+  dir: string,
+  name: string,
+  stream: vscode.ChatResponseStream,
+): Promise<void> {
+  const e = await readModel<EntityDetail>(dir, ["model", "entity", name]);
+  stream.markdown(`### ${e.name}\n`);
+  if (e.definition) stream.markdown(`${e.definition}\n\n`);
+  if (e.conceptual?.ontology) {
+    stream.markdown(`**Aligned to** \`${e.conceptual.ontology}\``);
+    if (e.conceptual.ontology_layer) stream.markdown(` _(${e.conceptual.ontology_layer} layer)_`);
+    stream.markdown("\n\n");
+  }
+  if (e.keys?.length) {
+    stream.markdown("**Keys**\n");
+    for (const k of e.keys) {
+      stream.markdown(`- ${k.type}: ${k.columns.join(", ") || "—"} (${k.name})\n`);
+    }
+    stream.markdown("\n");
+  }
+  if (e.attributes.length) {
+    stream.markdown("**Attributes**\n");
+    for (const a of e.attributes) {
+      const bits = [a.domain ?? "—", a.nullable ? "nullable" : "required"];
+      if (a.ontology) bits.push(`→ \`${a.ontology}\``);
+      stream.markdown(`- \`${a.name}\` (${bits.join(", ")})\n`);
+    }
+    stream.markdown("\n");
+  }
+  if (e.relationships.length) {
+    stream.markdown("**Relationships**\n");
+    for (const r of e.relationships) stream.markdown(`- ${r.from} → ${r.to} (${r.name})\n`);
+  }
+}
+
+async function renderList(dir: string, stream: vscode.ChatResponseStream): Promise<void> {
+  const rows = await readModel<EntityRow[]>(dir, ["model", "entities"]);
+  if (!rows.length) {
+    stream.markdown("The model has no entities yet.");
+    return;
+  }
+  stream.markdown(`The model has **${rows.length}** entities:\n\n`);
+  for (const r of rows) {
+    const area = r.subject_area ? ` _(${r.subject_area})_` : "";
+    stream.markdown(`- **${r.name}**${area} — ${r.attribute_count} attribute(s)\n`);
+  }
+}
+
+async function renderSummary(dir: string, stream: vscode.ChatResponseStream): Promise<void> {
+  const ctx = await readModel<ModelContext>(dir, ["model", "context"]);
+  stream.markdown(
+    `**${ctx.project}** — ${ctx.counts.entities} entities, ` +
+      `${ctx.counts.relationships} relationships, ${ctx.counts.subject_areas} subject area(s).\n\n`,
+  );
+  if (ctx.subject_areas.length) {
+    stream.markdown("**Subject areas**\n");
+    for (const sa of ctx.subject_areas) {
+      stream.markdown(`- **${sa.name}**: ${sa.entities.join(", ") || "(empty)"}\n`);
+    }
+    stream.markdown("\n");
+  }
+  if (ctx.unassigned_entities.length) {
+    stream.markdown(`_Not in any subject area:_ ${ctx.unassigned_entities.join(", ")}\n\n`);
+  }
+  stream.markdown(
+    "Ask about a specific entity by name, or use `@modelith /explain <entity>` and " +
+      "`@modelith /list`. For editing, switch Copilot Chat to **Agent mode** — the " +
+      "Modelith tools (create/update entities, ontology search) run there.",
+  );
+}
diff --git a/vscode/src/extension.ts b/vscode/src/extension.ts
index 8150ddf..c447d91 100644
--- a/vscode/src/extension.ts
+++ b/vscode/src/extension.ts
@@ -2,14 +2,18 @@ import * as path from "node:path";
 import * as vscode from "vscode";
 import type { LanguageClient } from "vscode-languageclient/node";
 import { CanvasManager } from "./canvasPanel";
+import { registerChatParticipant } from "./chatParticipant";
 import { executeLspCommand, startLsp } from "./lspClient";
+import { registerMcpProvider } from "./mcpProvider";
 import {
   findDbtProjectDir,
   findManifestPath,
   findMdl,
   findModelDir,
+  hasAiCommands,
   resetMdlCache,
   runMdl,
+  upgradeHint,
 } from "./mdl";
 import { registerSchemas } from "./schemas";
 
@@ -29,6 +33,12 @@ export async function activate(ctx: vscode.ExtensionContext): Promise<void> {
   canvas = new CanvasManager(out);
   ctx.subscriptions.push({ dispose: () => canvas.dispose() });
 
+  // AI surfaces: the bundled MCP server (Copilot Chat agent mode) and the
+  // `@modelith` chat participant (ask mode). Both are internally guarded against
+  // hosts that lack their API, so a failure here never blocks the LSP or canvas.
+  registerMcpProvider(ctx);
+  registerChatParticipant(ctx);
+
   // After a window reload VS Code restores our webview panels, but the `mdl serve`
   // child died with the old extension host, so the restored iframe points at a
   // dead port (blank). Re-hydrate the canvas (restart server + rewrite iframe);
@@ -99,6 +109,24 @@ export async function activate(ctx: vscode.ExtensionContext): Promise<void> {
         ctx.environmentVariableCollection.description =
           "Adds the Modelith `mdl` CLI to integrated terminals";
       }
+      // Version-skew handshake. The extension and the `mdl` CLI install separately,
+      // so a stale CLI can lack commands the AI surfaces (chat participant, MCP
+      // server) call. Probe once; if it's too old, tell the user how to upgrade
+      // THEIR install (inferred from the resolved path) — once, dismissibly, and
+      // without blocking anything. The per-call guards remain the backstop.
+      void hasAiCommands(bin, root).then((ok) => {
+        if (ok) return;
+        out.appendLine(`[mdl] ${bin.label} is missing the 'model'/'mcp' commands (out of date)`);
+        void vscode.window.showWarningMessage(
+          `Your Modelith CLI (${bin.label}) is out of date — it lacks commands the ` +
+            `chat and MCP features need. To fix, ${upgradeHint(bin)}, then reload VS Code.`,
+          "Reload Window",
+        ).then((pick) => {
+          if (pick === "Reload Window") {
+            void vscode.commands.executeCommand("workbench.action.reloadWindow");
+          }
+        });
+      });
     } catch (e) {
       out.appendLine(`[mdl] detection failed: ${e}`);
     }
diff --git a/vscode/src/mcpProvider.ts b/vscode/src/mcpProvider.ts
new file mode 100644
index 0000000..df21747
--- /dev/null
+++ b/vscode/src/mcpProvider.ts
@@ -0,0 +1,54 @@
+import * as vscode from "vscode";
+import { findMdl, findModelDir } from "./mdl";
+
+/** Register the bundled Modelith MCP server for Copilot Chat AGENT mode.
+ *
+ * Unlike the spec's Node-module sketch, the server IS the Python `mdl mcp`
+ * subcommand — `packages/core` is Python, so a Node module cannot compile against
+ * it, and every other integration here (LSP, canvas) already runs by spawning the
+ * `mdl` CLI. We register an stdio server that runs `mdl mcp --repo <workspace>`,
+ * resolving the binary through the same `findMdl` detection the LSP uses. One
+ * engine, invoked the one way this extension invokes it everywhere.
+ *
+ * A programmatically registered server has no implicit repo scope (a committed
+ * .vscode/mcp.json would), so we pass the workspace model dir explicitly.
+ *
+ * `registerMcpServerDefinitionProvider` only exists from VS Code 1.99+. An
+ * unguarded call has been seen crashing activation on older hosts and taking the
+ * LSP + canvas down with it — so optional-chain the API and no-op when absent. */
+export function registerMcpProvider(ctx: vscode.ExtensionContext): void {
+  const lm = vscode.lm as typeof vscode.lm & {
+    registerMcpServerDefinitionProvider?: (
+      id: string,
+      provider: {
+        provideMcpServerDefinitions: () => vscode.ProviderResult<unknown[]>;
+      },
+    ) => vscode.Disposable;
+  };
+  const Stdio = (
+    vscode as unknown as { McpStdioServerDefinition?: new (...a: unknown[]) => unknown }
+  ).McpStdioServerDefinition;
+  if (!lm?.registerMcpServerDefinitionProvider || !Stdio) return;
+
+  ctx.subscriptions.push(
+    lm.registerMcpServerDefinitionProvider("modelith.mcp", {
+      provideMcpServerDefinitions: async () => {
+        const dir = (await findModelDir()) ?? vscode.workspace.workspaceFolders?.[0]?.uri.fsPath;
+        if (!dir) return [];
+        // Resolve the mdl invocation the same way the LSP does (explicit setting →
+        // .venv → PATH → well-known → `uv run mdl`). `bin.args` is the prefix (e.g.
+        // ["run","mdl"] for uv); the server command follows.
+        let cmd: string;
+        let args: string[];
+        try {
+          const bin = await findMdl(dir);
+          cmd = bin.cmd;
+          args = [...bin.args, "mcp", "--repo", dir];
+        } catch {
+          return []; // mdl not found — the LSP path already surfaces this to the user
+        }
+        return [new (Stdio as new (...a: unknown[]) => unknown)("modelith", cmd, args)];
+      },
+    }),
+  );
+}
diff --git a/vscode/src/mdl.ts b/vscode/src/mdl.ts
index fa0687f..dcb7280 100644
--- a/vscode/src/mdl.ts
+++ b/vscode/src/mdl.ts
@@ -24,6 +24,17 @@ export async function findMdl(root: string): Promise<MdlBin> {
   if (explicit) candidates.push({ cmd: explicit, args: [], label: explicit });
   const venv = path.join(root, ".venv", "bin", "mdl");
   if (fs.existsSync(venv)) candidates.push({ cmd: venv, args: [], label: ".venv/bin/mdl" });
+  // A `.venv/bin/mdl` in a workspace folder, ranked ABOVE the global PATH install.
+  // This is what lets someone developing Modelith itself use their working-tree
+  // (editable) mdl instead of a stale `uv tool` global — the model dir is usually a
+  // demo/ subfolder with no venv, so the model-dir probe above misses the repo-root
+  // editable install. For a normal user (no workspace .venv) this adds nothing.
+  for (const folder of vscode.workspace.workspaceFolders ?? []) {
+    const wsVenv = path.join(folder.uri.fsPath, ".venv", "bin", "mdl");
+    if (wsVenv !== venv && fs.existsSync(wsVenv)) {
+      candidates.push({ cmd: wsVenv, args: [], label: `${folder.name}/.venv/bin/mdl` });
+    }
+  }
   candidates.push({ cmd: "mdl", args: [], label: "mdl (PATH)" });
   // GUI-launched VS Code on macOS/Linux has a minimal PATH that excludes the
   // per-user install locations a shell profile would add, so `mdl (PATH)` misses
@@ -92,6 +103,39 @@ export function runMdl(bin: MdlBin, args: string[], cwd: string): Promise<RunRes
   });
 }
 
+// --- CLI capability handshake (version skew) -----------------------------------
+
+/** Suggest the upgrade command that matches HOW this `mdl` was installed. The
+ * extension and the CLI are versioned independently (Marketplace .vsix vs a
+ * separate `mdl` install), so a stale CLI lacks commands the extension calls; we
+ * can only advise, never assume a package manager. Inferred from the resolved
+ * binary path — uv tool, pipx, a project .venv, or an unknown location. */
+export function upgradeHint(bin: MdlBin): string {
+  const c = `${bin.cmd} ${bin.args.join(" ")}`.toLowerCase();
+  if (c.includes("uv") && bin.args.includes("run")) {
+    return "run `uv sync` in the model repo (this is the workspace's own mdl)";
+  }
+  if (c.includes("/uv/tools/") || c.includes(".local/bin")) {
+    return "run `uv tool install --force modelith-dbt` (or `pipx upgrade modelith-dbt`)";
+  }
+  if (c.includes(".venv")) {
+    return "upgrade mdl in that virtualenv, e.g. `uv sync` or `pip install -U modelith-dbt`";
+  }
+  return (
+    "upgrade your mdl install — `uv tool install --force modelith-dbt`, " +
+    "`pipx upgrade modelith-dbt`, or `pip install -U modelith-dbt`"
+  );
+}
+
+/** Probe once that the resolved `mdl` has the commands the extension's AI surfaces
+ * (chat participant, MCP server) call. `mdl model` is the proxy: it shipped in the
+ * same release as `mdl mcp`, so its presence means the CLI is new enough. Returns
+ * true when capable; false when `mdl` is missing the command (stale) or unusable. */
+export async function hasAiCommands(bin: MdlBin, cwd: string): Promise<boolean> {
+  const r = await runMdl(bin, ["model", "--help"], cwd);
+  return r.code === 0;
+}
+
 // --- workspace discovery -------------------------------------------------------
 
 /** The model repo dir (contains mdl-project.yaml). Setting wins; else first hit. */