Openclaw Fcma MCP Server

@MohitDhawane

Basic information

About Openclaw Fcma MCP Server

6-tier AI memory system with 28 MCP tools — episodic memory, knowledge graphs, document storage, session ledger, auth & GDP 6-tier AI memory system with 28 MCP tools — episodic memory, knowledge graphs, document storage, session ledger, auth & GDPR complianceR compliance

Openclaw Fcma MCP Server config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "openclaw-fcma": {
      "command": "python",
      "args": [
        "-m",
        "openclaw.mcp_server"
      ]
    }
  }
}

Openclaw Fcma MCP Server tools

Tool list not available yet

This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

What is Openclaw Fcma?

Openclaw Fcma is a persistent memory layer for AI assistants, giving them structured recall across conversations. It uses a six-tier hierarchical architecture (scratchpad, journal, knowledge graph, document library, world state, and ledger) to store episodic memories, relationships, documents, live facts, and session summaries. Designed for developers and power users of Claude Desktop via the Model Context Protocol (MCP), it replaces flat retrieval-augmented generation (RAG) with surprise-driven learning and built-in user isolation.

How to use Openclaw Fcma?

Clone the repository, install dependencies with pip install -e ".[dev]", and add the server to Claude Desktop’s claude_desktop_config.json using python -m openclaw.mcp_server. After restarting Claude, use any of the 28 MCP tools—like memory_store, mind_add_node, library_upload, or user_register—to store and recall memories. The server can also run in HTTP mode with --transport http.

Key features of Openclaw Fcma

Six-tier hierarchical memory (L1–L6)
28 MCP tools for episodic, graph, document, and key-value storage
Knowledge graph with neural decay and pruning
Surprise-driven memory updates with salience scoring
Built-in user management, JWT authentication, and rate limiting
GDPR-compliant data export and cascade deletion
Runs on FAISS vector store, SQLite, and Neo4j

Use cases of Openclaw Fcma

Give Claude persistent memory across conversations—remember user names, projects, and preferences.
Store and semantically search documents (PDF, text, code) with two-stage retrieval and re-ranking.
Build and query a knowledge graph of entities and relationships for complex reasoning.
Track live facts (key-value state) and session summaries for long-running projects.
Manage multi-user environments with per-user isolation, API keys, and audit logging.

Frequently asked questions

What dependencies does Openclaw Fcma require?

Python 3.10+, FAISS, SQLAlchemy, Neo4j (for knowledge graph), and optional libraries for document ingestion. All dependencies are installed via `pip install -e ".[dev]"`.

Where is data stored?

Data is stored locally under a `data/` directory: SQLite database (`openclaw.db`), FAISS vector indexes (`journal/`, `library/`), Markdown session summaries (`ledger/`), and knowledge graph exports (`graph_exports/`). All paths are configurable via environment variables.

How does authentication work?

Openclaw Fcma uses JWT tokens with 24-hour expiry, auto-generated API keys (`fcma_xxxx…`), salted SHA-256 password hashing, and sliding-window rate limiting (default 60 requests per minute, configurable via `FCMA_RATE_LIMIT`).

What transports does the MCP server support?

The server supports stdio transport (default) and HTTP transport (`--transport http`). Use the MCP Inspector for debugging with `mcp dev openclaw/mcp_server.py`.

Can I export or delete my data for GDPR compliance?

Yes. The tools `user_data_export` and `user_data_delete` allow full export and cascade deletion of all user data, with an audit trail available via `audit_log`.

Comments

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