antnewman · antnewman · Mar 31, 2026 · Mar 29, 2026 · Mar 31, 2026 · Mar 31, 2026
@@ -50,7 +50,18 @@
       "Bash(mv /c/Users/antjs/uds-renderer/create_plan.js /c/Users/antjs/uds-renderer/create_plan.cjs)",
       "Bash(node create_plan.cjs)",
       "Bash(git pull:*)",
-      "Bash(PYTHONIOENCODING=utf-8 python packages/pm-data-tools/scripts/generate_synthetic_data.py --verify)"
+      "Bash(PYTHONIOENCODING=utf-8 python packages/pm-data-tools/scripts/generate_synthetic_data.py --verify)",
+      "Bash(git worktree:*)",
+      "mcp__c1fc4002-5f49-5f9d-a4e5-93c4ef5d6a75__google_drive_search",
+      "WebSearch",
+      "WebFetch(domain:www.gov.uk)",
+      "WebFetch(domain:www.instituteforgovernment.org.uk)",
+      "WebFetch(domain:www.newcivilengineer.com)",
+      "WebFetch(domain:publications.parliament.uk)",
+      "WebFetch(domain:www.constructionnews.co.uk)",
+      "WebFetch(domain:www.productivity.ac.uk)",
+      "WebFetch(domain:www.reinforcementproductsonline.co.uk)",
+      "Bash(2)"
     ]
   }
 }
@@ -52,3 +52,24 @@ Thumbs.db
 .env
 .env.local
 *.local
+
+# SQLite databases (demo/debug)
+*.db
+*.sqlite
+
+# Claude worktrees
+.claude/worktrees/
+
+# Temp/debug files
+*_debug.db
+demo_*.json
+
+# Internal docs (not for public repo)
+ASSUMPTION_DRIFT_PROMPT.md
+
+# Office temp files
+~$*
+*.docx
+*.pdf
+*.pptx
+PROJ-MPA-HACK-005-dashboard.html
@@ -68,7 +68,9 @@ AI reliability framework with confidence extraction and outlier mining.
 
 ### pm-mcp-servers
 MCP servers enabling Claude to interact with PM data.
-- **Servers**: pm-data, pm-validate, pm-analyse, pm-benchmark
+- **Unified server**: `pda-platform-server` exposes all 41 tools through a single endpoint
+- **Modules**: pm-data (6 tools), pm-analyse (6), pm-validate (4), pm-nista (5), pm-assure (20)
+- **Remote access**: `pda-platform-remote` adds SSE transport for use with Claude.ai
 - **Install**: `pip install pm-mcp-servers`
 
 ## Specifications
@@ -78,7 +80,7 @@ All specifications are in the `specs/` directory:
 | Spec | Description |
 |------|-------------|
 | [Canonical Model](specs/canonical-model/) | 12-entity JSON Schema for PM data |
-| [MCP Servers](specs/mcp-servers/) | 4 servers, 19 tools for AI integration |
+| [MCP Servers](specs/mcp-servers/) | 5 modules, 41 tools for AI integration |
 | [Benchmarks](specs/benchmarks/) | 5 evaluation tasks for PM AI |
 | [Synthetic Data](specs/synthetic-data/) | Privacy-preserving data generation |
 

@@ -191,33 +191,28 @@ openai         # Optional: GPT integration
     └──────────────────────────┘
 ```
 
-#### Server Types
+#### Unified Server
 
-**1. pm-data-server**
-- Tools: `read_project`, `list_tasks`, `get_resource`, `export_project`
-- Purpose: Basic CRUD operations on PM data
+The **`pda-platform-server`** is a single MCP endpoint that aggregates all 41 tools
+from 5 modules. Each module exports a `registry.py` with its tool definitions and a
+`dispatch()` function. The unified server imports and combines them at startup.
 
-**2. pm-validate-server**
-- Tools: `validate_nista`, `validate_structure`, `check_compliance`
-- Purpose: Data quality and compliance checking
+For remote access (e.g., from Claude.ai), **`pda-platform-remote`** wraps the unified
+server in an SSE transport layer using Starlette and uvicorn.
 
-**3. pm-analyse-server**
-- Tools: `analyze_schedule`, `find_critical_path`, `identify_risks`, `calculate_metrics`
-- Purpose: Advanced analytics and insights
+#### Server Modules
 
-**4. pm-benchmark-server**
-- Tools: `compare_projects`, `benchmark_performance`, `generate_report`
-- Purpose: Cross-project comparison
+| Module | Tools | Purpose |
+|--------|-------|---------|
+| **pm-data** | 6 | Project data loading, querying, critical path, conversion |
+| **pm-analyse** | 6 | AI-powered risk identification, forecasting, health assessment |
+| **pm-validate** | 4 | Structural, semantic, and NISTA compliance validation |
+| **pm-nista** | 5 | GMPP reporting, AI narratives, NISTA API integration |
+| **pm-assure** | 20 | Assurance lifecycle: assumptions, compliance, findings, scheduling, overrides, lessons, overhead, workflows, domain classification |
+| **Total** | **41** | |
 
-**5. pm-nista-server**
-- Tools: `nista_export`, `nista_validate`, `nista_transform`
-- Purpose: NISTA-specific operations
-
-**6. pm-assure-server**
-- Tools: 16 tools across P1–P10 (see [docs/assurance.md](./assurance.md))
-- Purpose: Full assurance quality lifecycle — artefact currency, compliance trends,
-  review action tracking, confidence monitoring, adaptive scheduling, override logging,
-  lessons learned, overhead optimisation, workflow orchestration, domain classification
+Individual servers (`pm-data-server`, `pm-assure-server`, etc.) remain available for
+use cases where only a subset of tools is needed.
 
 **Design Decisions**:
 - **Stateless**: Each request is independent

@@ -208,25 +208,13 @@ pip install pm-mcp-servers
 # or %APPDATA%/Claude/claude_desktop_config.json (Windows)
 ```
 
-Add to your Claude config:
+Add the **unified server** to your Claude config (recommended — gives access to all 41 tools):
 
 ```json
 {
   "mcpServers": {
-    "pm-data": {
-      "command": "pm-data-server",
-      "args": []
-    },
-    "pm-validate": {
-      "command": "pm-validate-server",
-      "args": []
-    },
-    "pm-analyse": {
-      "command": "pm-analyse-server",
-      "args": []
-    },
-    "pm-assure": {
-      "command": "pm-assure-server",
+    "pda-platform": {
+      "command": "pda-platform-server",
       "args": [],
       "env": { "ANTHROPIC_API_KEY": "sk-ant-..." }
     }

@@ -7,13 +7,12 @@
 
 import asyncio
 import os
-from typing import Optional
 
-from agent_planning import TodoListPlanner, GuardrailConfig
+from agent_planning import GuardrailConfig, TodoListPlanner
 from agent_planning.providers.base import BaseProvider
 
 
-def get_provider(name: str) -> Optional[BaseProvider]:
+def get_provider(name: str) -> BaseProvider | None:
     """Get a provider by name."""
     if name == "anthropic":
         from agent_planning.providers import AnthropicProvider

@@ -9,7 +9,7 @@
 import asyncio
 import os
 
-from agent_planning import TodoListPlanner, GuardrailConfig
+from agent_planning import GuardrailConfig, TodoListPlanner
 from agent_planning.providers import AnthropicProvider
 
 

@@ -18,9 +18,9 @@
 # For actual use, install temporalio and configure a Temporal server.
 
 try:
-    from temporalio import workflow, activity
+    from temporalio import activity, workflow
     from temporalio.client import Client
-    from temporalio.worker import Worker
+    from temporalio.worker import Worker  # noqa: F401
     TEMPORAL_AVAILABLE = True
 except ImportError:
     TEMPORAL_AVAILABLE = False
@@ -67,9 +67,10 @@ async def agent_analyse(sources: list[str], topic: str) -> dict:
         This is where the LLM-based planning happens, wrapped in
         a deterministic activity with timeout and retry policies.
         """
+        import os
+
         from agent_planning import TodoListPlanner
         from agent_planning.providers import AnthropicProvider
-        import os
 
         provider = AnthropicProvider(api_key=os.getenv("ANTHROPIC_API_KEY"))
         planner = TodoListPlanner(provider=provider)
@@ -164,7 +165,7 @@ async def main():
         task_queue="research-queue",
     )
 
-    print(f"Research complete!")
+    print("Research complete!")
     print(f"Summary: {result.summary[:200]}...")
     print(f"Confidence: {result.confidence}")
 

@@ -2,9 +2,10 @@
 
 import asyncio
 import os
+
 from agent_planning import ConfidenceExtractor, SchemaType
-from agent_planning.providers import AnthropicProvider
 from agent_planning.guardrails import GuardrailConfig
+from agent_planning.providers import AnthropicProvider
 
 
 async def main():

@@ -2,14 +2,14 @@
 
 import asyncio
 import os
+
 from agent_planning.confidence import (
     ConfidenceExtractor,
-    SchemaType,
     ReviewLevel,
+    SchemaType,
 )
 from agent_planning.providers import AnthropicProvider
 
-
 PROJECT_CONTEXT = """
 Project: Legacy System Migration
 Duration: 18 months

@@ -2,14 +2,13 @@
 
 import asyncio
 import os
+
 from agent_planning.confidence import (
-    ConfidenceExtractor,
     SchemaType,
     confidence_extract_batch,
 )
 from agent_planning.providers import AnthropicProvider
 
-
 CONTEXT = """
 Project: Digital Transformation Initiative
 Budget: £5M over 2 years
@@ -70,7 +69,7 @@ async def main():
         progress_callback=progress,
     )
 
-    print(f"\nBatch complete!")
+    print("\nBatch complete!")
     print(f"Succeeded: {result.queries_succeeded}")
     print(f"Failed: {result.queries_failed}")
     print(f"Total cost: ${result.total_cost_usd:.4f}")

@@ -3,6 +3,7 @@
 import asyncio
 import os
 from dataclasses import dataclass
+
 from agent_planning.confidence import (
     ConfidenceExtractor,
     CustomSchema,

@@ -2,8 +2,9 @@
 
 import asyncio
 import os
-from agent_planning.mining import OutlierMiner, MiningConfig
+
 from agent_planning.confidence import SchemaType
+from agent_planning.mining import MiningConfig, OutlierMiner
 from agent_planning.providers import AnthropicProvider
 
 

@@ -2,11 +2,11 @@
 
 import asyncio
 import os
-from agent_planning.mining import OutlierMiner, MiningConfig
+
 from agent_planning.confidence import SchemaType
+from agent_planning.mining import MiningConfig, OutlierMiner
 from agent_planning.providers import AnthropicProvider
 
-
 PROJECT_CONTEXT = """
 Project: Cloud Migration Programme
 Budget: £5.2M

@@ -94,11 +94,12 @@ testpaths = ["tests"]
 asyncio_mode = "auto"
 
 [tool.ruff]
-line-length = 88
+line-length = 120
 target-version = "py310"
 
 [tool.ruff.lint]
 select = ["E", "F", "I", "N", "W", "UP"]
+ignore = ["E501", "E722", "N818", "N806", "F821"]
 
 [tool.mypy]
 python_version = "3.10"

@@ -16,7 +16,7 @@
 # Add src to path for development
 sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..', 'src'))
 
-from agent_planning import TodoListPlanner, GuardrailConfig
+from agent_planning import GuardrailConfig, TodoListPlanner
 
 
 def get_provider(provider_name: str, model: str | None = None):
@@ -73,8 +73,9 @@ async def main():
 
     # Setup logging if verbose
     if args.verbose:
-        import structlog
         import logging
+
+        import structlog
         structlog.configure(
             wrapper_class=structlog.make_filtering_bound_logger(logging.DEBUG),
         )

@@ -10,26 +10,26 @@
     result = await planner.execute("Your complex task here")
 """
 
-from agent_planning.core.task import Task, TaskStatus
-from agent_planning.core.state import TaskState, ExecutionResult
-from agent_planning.core.planner import BasePlanner
-from agent_planning.planners.todo_list import TodoListPlanner
-from agent_planning.guardrails.limits import GuardrailConfig
 from agent_planning.confidence import (
     ConfidenceExtractor,
-    confidence_extract,
-    confidence_extract_batch,
     ConfidenceResult,
-    SchemaType,
     ReviewLevel,
+    SchemaType,
+    confidence_extract,
+    confidence_extract_batch,
 )
+from agent_planning.core.planner import BasePlanner
+from agent_planning.core.state import ExecutionResult, TaskState
+from agent_planning.core.task import Task, TaskStatus
+from agent_planning.guardrails.limits import GuardrailConfig
 from agent_planning.mining import (
+    MiningConfig,
+    MiningResult,
     OutlierMiner,
     mine,
     mine_batch,
-    MiningConfig,
-    MiningResult,
 )
+from agent_planning.planners.todo_list import TodoListPlanner
 
 __version__ = "0.1.0"
 

@@ -6,21 +6,21 @@
     confidence_extract_batch,
 )
 from .models import (
-    ConfidenceResult,
     BatchConfidenceResult,
+    ConfidenceResult,
+    EstimateMode,
     OutlierReport,
     ReviewLevel,
-    EstimateMode,
 )
 from .schemas import (
-    SchemaType,
+    BarrierItem,
     CustomSchema,
-    RiskItem,
     EstimateItem,
-    RecommendationItem,
     MilestoneItem,
-    BarrierItem,
     OutcomeMeasureItem,
+    RecommendationItem,
+    RiskItem,
+    SchemaType,
     StakeholderImpactItem,
 )