ThreadKeeper

ThreadKeeper is a local-first productivity and memory layer for MCP-enabled AI agents.

It gives an AI model a durable, typed interface for capturing ideas, managing tasks, scheduling reminders, running focus sessions, saving project context, recording decisions, and recalling explicit memory. Canonical data stays in a user-owned SQLite database.

ThreadKeeper is not a model, chat application, hosted account, calendar, or email integration. The MCP host owns the conversation and model; ThreadKeeper provides the durable tools and data layer.

How MCP connects the model to ThreadKeeper

sequenceDiagram
  actor User
  participant Host as MCP host
  participant Model as AI model
  participant TK as ThreadKeeper
  participant DB as SQLite

  User->>Host: Natural-language request
  Host->>TK: Discover tools, resources, and prompts
  Host->>Model: User request plus MCP tool schemas
  Model->>Host: Structured tool call
  Host->>TK: tools/call with JSON arguments
  TK->>TK: Validate schema, dates, IDs, and state
  TK->>DB: Read or write canonical records
  TK-->>Host: Text plus structuredContent
  Host-->>Model: Tool result
  Model-->>User: Evidence-based response

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The integration works like this:

1. An MCP host launches ThreadKeeper over stdio or connects to its authenticated loopback HTTP endpoint.
2. The host discovers typed tools with JSON Schemas and read/write annotations.
3. The model chooses the narrowest matching tool from the user's intent.
4. ThreadKeeper validates the arguments and domain rules before touching storage.
5. The server returns both concise text and structured data.
6. The model summarizes the result or asks for missing information or confirmation.

ThreadKeeper exposes:

• 20 core tools;
• four bounded read-only resources;
• versioned daily_review and weekly_review prompt templates;
• optional profiles for memory review, weekly review, reading queues, habits, checklists, shopping, locations, and productivity insights.

See the tool catalogue for the complete contract.

Use cases

User intent	Example prompt	Model selects
Capture something for later	“Capture this idea: add offline export.”	inbox_capture
Create a commitment	“Create a high-priority task to test exports tomorrow.”	task_create
Schedule an alert	“Remind me every Monday at 9 AM to review backups.”	reminder_create
Plan the day	“What needs attention today in Asia/Kolkata?”	daily_brief
Work in a focus block	“Start a 45-minute focus session for project alpha.”	focus_start
Save project continuity	“Save where we stopped on alpha; next action is testing sync.”	context_save
Preserve a rationale	“Record that we chose SQLite because it is local-first.”	decision_record
Store a durable preference	“Remember that I prefer concise status summaries.”	memory_remember
Retrieve prior knowledge	“What are my morning work preferences?”	memory_recall
Search across records	“Find protocol work across tasks and decisions.”	global_search

Use explicit verbs because each one represents a different durable record:

• “capture” or “save for later” means inbox;
• “create” or “add as a task” means task;
• “remind me” means reminder;
• “remember that” means memory;
• “save project progress” means context;
• “record why we chose” means decision.

An inbox item is not automatically promoted to a task. To convert one later, say: “Create a task from inbox item <exact-id>.”

Core workflows

Capture now, process later

Capture this as an inbox task candidate: benchmark FTS.

Later:

Create a high-priority task from inbox item <id>: benchmark FTS by Friday.

Plan and execute a day

Give me today's brief in Asia/Kolkata.

Review the returned tasks, reminders, active focus session, project context, inbox count, and relevant memories. Start work with focus_start, then complete tasks or resolve reminders as work finishes.

Pause and resume a project

Before stopping:

Save the current context for ThreadKeeper.
Progress: protocol core is complete.
Next action: implement memory retrieval.
Blocker: none.

Later:

Resume the ThreadKeeper project and show the latest context.

Context snapshots are immutable, so previous resume points remain available.

Preserve decisions separately from memory

Record the decision to use SQLite because it is local and portable.

Use decision_search to find the authoritative rationale later. A memory may reference a decision, but it does not replace the decision record.

Store and correct explicit memory

Remember that I prefer morning focus blocks.

Later:

Correct that memory: I now prefer afternoon focus blocks.

Corrections preserve the history and validity interval of the previous memory. Permanent deletion requires the exact memory ID and explicit confirmation.

Model integration boundaries

ThreadKeeper helps the model use durable state without making the model autonomous:

• read-only requests use list, search, get, brief, or recall operations;
• mutations require the correct domain tool;
• existing records are changed by exact ID;
• destructive memory operations require confirmation;
• stored records and tool results are data, not instructions;
• missing required fields should trigger a question, not a guess;
• ThreadKeeper never silently turns an inbox capture into a task;
• ThreadKeeper does not make calendar, email, payment, ordering, or real-time location claims.

The model should use domain-specific tools when available:

• task_list, not global_search, for task queries;
• reminder_list, not daily_brief, for reminder-only queries;
• context_get, not memory recall, for project resume points;
• decision_search, not global search, for authoritative decisions;
• memory_recall, not global search, for personal preferences and temporal memory.

Install and verify

Go 1.26.5 or newer is required.

go build -trimpath -o threadkeeper ./cmd/threadkeeper
./threadkeeper doctor
go test ./...

The default database is stored in the operating system's user configuration directory. Override it with:

./threadkeeper doctor --db /absolute/path/threadkeeper.db

Configure an MCP client

{
  "mcpServers": {
    "threadkeeper": {
      "command": "/absolute/path/threadkeeper",
      "args": [
        "serve",
        "--db",
        "/absolute/path/threadkeeper.db",
        "--time-zone",
        "Asia/Kolkata"
      ]
    }
  }
}

After restarting the client, it should discover ThreadKeeper's tools, resources, and review prompts.

The server implements MCP 2025-11-25 over newline-delimited stdio and authenticated loopback Streamable HTTP. Stdio stdout contains protocol messages only; diagnostics use stderr.

For complete setup, HTTP configuration, maintenance, troubleshooting, and security details, see Setup, usage, and use cases.

Read-only resources

MCP clients may read these bounded resources without selecting a mutating tool:

Resource	Purpose
threadkeeper://today	Current structured daily brief
threadkeeper://inbox	Latest active inbox items
threadkeeper://memory/recent	Recent non-sensitive memories
threadkeeper://system/status	Version, capabilities, and index health

Privacy and limitations

The default mode is local and single-user. Outbound access is disabled unless embedding, candidate-extraction, or bookmark metadata providers are explicitly configured.

ThreadKeeper stores canonical records locally, uses SQLite and FTS5, and keeps logs and audits free of raw tool input where possible. The local database is not application-encrypted; use operating-system disk encryption and account security.

Remote mode is a separate explicitly launched native-TLS/OAuth profile with physically owner-scoped databases. It is not a hosted ThreadKeeper account.

Documentation

• Setup, usage, and use cases
• Tool catalogue
• Architecture
• Protocol
• Memory model
• Evaluation
• Security
• Contributing

License

ThreadKeeper is licensed under the Apache License 2.0.