MCP Tools Reference

Eidet exposes 6 tools via the Model Context Protocol — the core session-flow tools AI agents call directly. Advanced operations (intake, consolidation, maintenance, version history, curation, pack import/export) run server-side and are reached via the REST API and CLI, keeping the agent-facing surface small.

Transports

Transport	Command	Use case
stdio	eidet mcp	Local AI clients (Claude Code, Cursor)
HTTP	POST http://localhost:19380/mcp	Network MCP clients

Tool Listing

eidet_store

Store a new memory.

{
  "repo": "P:\\MyProject",
  "content": "The UserService caches results in Redis with a 5-minute TTL",
  "type": "observation",
  "tags": ["redis", "caching", "user-service"],
  "importance": 0.6,
  "source": "claude-session",
  "session_id": "sess-abc123",
  "supersedes": null
}

Returns: { "id": "memories/...", "success": true } or rejection reason.

eidet_recall

Recall memories by semantic query (hybrid vector + full-text search).

{
  "repo": "P:\\MyProject",
  "query": "how does caching work",
  "limit": 10,
  "type": "insight"
}

Returns: Array of scored results with content, type, tags, importance, staleness warnings.

eidet_context

Get the compact L0+L1 session context. This is typically the first tool an agent calls.

{
  "repo": "P:\\MyProject"
}

Returns: A text block under 600 tokens with memory counts and top-scored memories.

On first call for a new repo (when no memories exist), automatically triggers intake to scan project files.

eidet_forget

Soft-delete a memory with an optional reason.

{
  "id": "memories/P--MyProject/observation/abc123",
  "reason": "This information is outdated"
}

Creates an audit trail observation recording what was forgotten and why.

eidet_feedback

Report whether a recalled memory was useful.

{
  "memory_id": "memories/P--MyProject/insight/abc123",
  "was_used": true
}

• was_used: true — Echo: boosts importance and confidence
• was_used: false — Fizzle: reduces importance and confidence

eidet_link

Create a cross-repo link between two memories.

{
  "source_id": "memories/P--ProjectA/insight/abc",
  "target_id": "memories/P--ProjectB/insight/def",
  "relation": "depends-on"
}

Server-side operations

These are deliberately not on the MCP surface — agents rarely need them inline, and keeping them off the tool list reduces session overhead. They run automatically (scheduler/maintenance pipeline) or are invoked by an operator via the REST API, CLI, or Web UI:

Operation	REST endpoint	Notes
Intake	POST /api/eidet/intake	Also fires automatically on first eidet_context for a new repo
Consolidate	POST /api/eidet/consolidate	Also runs as a maintenance stage
Maintenance	POST /api/maintenance	TTL expiry, dedup, decay, enrichment, auto-consolidation
Version history	GET /api/eidet/{id} (version chain)	Supersession ancestry
Curation / edit	PUT /api/eidet/{id}	Versioned on content change
Pack export	POST export endpoints	Portable .eidet pack
Pack import	mount-as-layer endpoints	Import + mount a pack

Agent Integration Pattern

A typical AI agent session flow:

1. Session starts
2. Agent calls eidet_context → gets L0+L1 summary
3. Agent works on task, calls eidet_recall as needed
4. Agent discovers something → calls eidet_store
5. Agent uses a recalled memory → calls eidet_feedback(was_used: true)
6. Agent finds a memory wrong → calls eidet_forget with reason
7. Session ends (memories persist for next session)

CLAUDE.md Instructions

Use eidet instructions to generate ready-made instructions for your CLAUDE.md:

# Print to stdout
eidet instructions --print

# Install into ~/.claude/CLAUDE.md
eidet instructions --install

# Create in project root
eidet instructions --project