engrava-mcp 0.5.0

Model Context Protocol server for Engrava — expose an agent memory database to any MCP client over stdio.

Engrava MCP

The Model Context Protocol server for Engrava — expose an agent memory database to any MCP client (Claude Desktop, Claude Code, Cursor, Windsurf, VS Code, …) over stdio.

engrava-mcp is a standalone, runnable package that consumes Engrava's public API. It is the one way to run Engrava as a memory server; the engrava library itself ships no MCP code.

uvx engrava-mcp        # run the server (no install step)
# or
pip install engrava-mcp
engrava-mcp            # spawned by your MCP client over stdio

Installing engrava-mcp pulls in engrava transitively, so you also get the import engrava library in the same environment.

Compatibility

engrava-mcp follows Engrava's version: engrava-mcp X.Y.z targets engrava X.Y and requires engrava >=X.Y,<X.(Y+1). This is a one-way version mirror for legibility — not a lockstep: Engrava releases on its own cadence, and engrava-mcp patch releases are independent.

engrava-mcp	Works with engrava
0.5.x	>=0.5,<0.6

The dependency range is the source of truth. Normal installs resolve a compatible engrava automatically; if you pin engrava yourself, keep it within that range. If no matching engrava-mcp exists yet for a newer engrava (e.g. a fresh engrava 0.6), that pairing is not yet verified/supported — not broken; stay on a supported pair until a matching engrava-mcp ships.

Which package do I want?

Goal	Install
Build on the Engrava Python API (memory DB in your own code)	pip install engrava
Run Engrava as a memory server for an MCP client	uvx engrava-mcp (or pip install engrava-mcp)

There is no third option.

Migrating from engrava[mcp]

The server used to ship inside Engrava as the engrava[mcp] extra and an in-engrava engrava-mcp command. As of Engrava 0.5.0 it lives here instead.

Before	After
pip install "engrava[mcp]"	pip install engrava-mcp (or uvx engrava-mcp)
engrava-mcp (installed by engrava)	engrava-mcp (installed by this package)
client mcp.json: "command": "engrava-mcp"	client mcp.json: "command": "uvx", "args": ["engrava-mcp"]

• Watch out: pip install "engrava[mcp]" against Engrava 0.5 does not fail — pip ignores the now-unknown extra and quietly installs bare engrava, so it can look like the server installed when it did not. Install engrava-mcp instead.
• Update any pinned requirement strings (engrava[mcp]>=...) to depend on engrava-mcp, not just reinstall.
• Your store configuration is unchanged — the same engrava.yaml / env vars work exactly as before (see Configuration).

Configuration

The server resolves its store from environment variables, in priority order:

Variable	Meaning
ENGRAVA_MCP_CONFIG	Path to an engrava.yaml. Built with the full configuration — embedding provider, vector backend, journal, TTL. Recommended.
ENGRAVA_DB_PATH	Path to a bare SQLite database file. Zero-config quick-start; no embedding provider is configured, so semantic (vector) search is inert — full-text search, the graph, MindQL, and the audit trail still work.
ENGRAVA_MCP_READ_ONLY	When set to 1 / true / yes, the write tools are not registered, so the server exposes a read-only surface.

Recommended: give the MCP server the same engrava.yaml your application uses. The yaml is the only place to declare an embedding provider (and its model / key), which the server needs to embed a new query at search time for semantic search. With only ENGRAVA_DB_PATH set, the server logs a startup warning that semantic search is inert and points you at ENGRAVA_MCP_CONFIG.

Example engrava.yaml

db_path: ./memory.db
embeddings:
  provider: openai            # or: ollama, sentence-transformer, huggingface
  model: text-embedding-3-small
  api_key: ${OPENAI_API_KEY}

Client setup

Point your MCP client at the server over stdio. For example, a typical mcp.json entry:

{
  "mcpServers": {
    "engrava": {
      "command": "uvx",
      "args": ["engrava-mcp"],
      "env": {
        "ENGRAVA_MCP_CONFIG": "/absolute/path/to/engrava.yaml"
      }
    }
  }
}

Use ENGRAVA_DB_PATH instead of ENGRAVA_MCP_CONFIG for the zero-config quick-start, and add "ENGRAVA_MCP_READ_ONLY": "1" for an app-writes / agent-reads deployment.

Running without uvx

engrava-mcp                  # console script
python -m engrava_mcp        # module run
python -m engrava_mcp.server # module run (server module directly)

Optional providers

The default install supports the vector backend and HTTP-based embedding providers (OpenAI / Ollama) once configured in the yaml. Heavier providers are opt-in extras that mirror Engrava's own extras:

uvx --from "engrava-mcp[local]"  engrava-mcp   # sentence-transformers (local model)
uvx --from "engrava-mcp[hf]"     engrava-mcp   # HuggingFace Inference API
uvx --from "engrava-mcp[openai]" engrava-mcp   # OpenAI-compatible embeddings deps
uvx --from "engrava-mcp[ollama]" engrava-mcp   # Ollama embeddings deps

The surface

• Tools (11): get_thought, search_memory, search_keywords, list_memory, query_memory, memory_stats (read); store_thought, update_thought, link_thoughts, delete_thought, delete_edge (write, gated by ENGRAVA_MCP_READ_ONLY).
• Resources (3): engrava://thought/{thought_id}, engrava://stats, engrava://recent.
• Prompts (3): summarize_recent_memory, find_related, reflect_on_topic.

query_memory accepts only MindQL FIND queries; raw SQL and every other command are rejected.

Development

pip install -e ".[dev]"
ruff check src/ tests/
ruff format --check src/ tests/
mypy --strict src/
pytest --cov --cov-fail-under=90

License

MIT