Memanto MCP Server

Memanto MCP Server

Persistent semantic memory for any MCP-compatible agent.

This package exposes Memanto's memory primitives —remember, recall, answer, and friends — asModel Context Protocol (MCP) tools so anyMCP client (Claude Desktop, Cursor, Windsurf, Cline, Continue, Goose,custom agents, …) can plug into long-term memory in a single config line.

One Moorcheh API key → typed semantic memory across every agent thatshares the namespace, with sub-90 ms retrieval, conflict detection, andzero ingestion latency.

Install

pip install memanto-mcp

Requires Python 3.10+ and a Moorcheh API key(free tier: 100K ops/month).

Quick start (Claude Desktop)

1. Get a Moorcheh API key from the console.
2. Edit claude_desktop_config.json (Settings → Developer → Edit Config):

{
  "mcpServers": {
    "memanto": {
      "command": "memanto-mcp",
      "env": {
        "MOORCHEH_API_KEY": "mch_xxxxxxxxxxxxxxxxxx",
        "MEMANTO_DEFAULT_AGENT_ID": "my-assistant"
      }
    }
  }
}

3. Restart Claude Desktop. Ask it to "remember that I prefer conciseanswers" — then in a brand-new chat tomorrow ask "what do I prefer?".

The first call auto-creates the my-assistant agent and namespace; everysubsequent call reuses the same persistent memory.

Quick start (Cursor / Windsurf / Cline / Continue / Goose)

Most clients consume a config file in thestandard MCP shape.The same JSON snippet works almost verbatim:

{
  "mcpServers": {
    "memanto": {
      "command": "memanto-mcp",
      "env": {
        "MOORCHEH_API_KEY": "mch_xxxxxxxxxxxxxxxxxx",
        "MEMANTO_DEFAULT_AGENT_ID": "cursor-workspace"
      }
    }
  }
}

Available tools

The server registers 7 memory tools by default. SetMEMANTO_EXPOSE_ADMIN=true to also expose 4 agent-management tools.

Memory tools (always on)

Agent admin tools (opt-in)

Enabled when MEMANTO_EXPOSE_ADMIN=true:

Memory types accepted by remember / batch_remember:fact, preference, goal, decision, artifact, learning, event,instruction, relationship, context, observation, commitment,error.

Provenance values: explicit_statement, inferred, corrected,validated, observed, imported.

Configuration

All config is via environment variables (load order: process env →.env file in the working directory).

CLI flags (memanto-mcp --transport sse --port 9000) override env vars.

Running over HTTP / SSE

For remote clients or multi-process setups, run the server over a networktransport:

# Streamable HTTP (recommended modern transport)
memanto-mcp --transport streamable-http --host 0.0.0.0 --port 8765

# Server-Sent Events (older, still widely supported)
memanto-mcp --transport sse --host 0.0.0.0 --port 8765

Then point your client at http://your-host:8765/mcp (or whatever path thechosen transport advertises). Pair with a reverse proxy + auth forproduction deployments — the server itself authenticates upstream toMoorcheh using your API key but does not authenticate inbound MCPclients.

How it works

┌──────────────┐    MCP/stdio    ┌──────────────────┐    Moorcheh API    ┌─────────────┐
│ Claude / IDE │ ──────────────► │  memanto-mcp     │ ────────────────► │   Moorcheh  │
│   (client)   │ ◄────────────── │  (this package)  │ ◄──────────────── │   Service   │
└──────────────┘    tool calls   └──────────────────┘    HTTPS+API key   └─────────────┘
                                          │
                                          └─ uses memanto.cli.client.SdkClient
                                             (same client the Memanto CLI uses)

• On startup, settings are validated; the API key is verified lazily onfirst tool call.
• On the first memory tool invocation for a given agent, the serverensures the agent exists (auto-creates if needed) and activates a JWTsession. Sessions auto-renew before expiry, so long-running MCPconnections never hit a session-expired error mid-conversation.
• The server intentionally keeps the session alive on shutdown: JWTsessions are TTL-bound and other Memanto clients (CLI, REST) may wantto share them.

Programmatic embedding

If you're building a custom MCP host or wiring this server into a largerprocess, you can construct the FastMCP instance yourself:

from memanto_mcp import MCPServerSettings, build_server

settings = MCPServerSettings()  # reads env / .env
mcp = build_server(settings)

# Add your own tools alongside Memanto's, then run.
mcp.run(transport="stdio")

Troubleshooting

License

MIT — same as the Memantoproject. See LICENSE.

Links

• Memanto — the memory agent itself
• Moorcheh — the no-indexing semantic DB underneath
• Model Context Protocol spec
• Anthropic MCP Python SDK