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MCP server for Cathedral — persistent memory and drift detection for AI agents

Project description

cathedral-mcp

Persistent memory and behavioural drift detection for AI agents — via Model Context Protocol.

Give your Claude, Cursor, or Continue agent a memory that survives across sessions, model upgrades, and restarts. Cathedral stores memories, detects when your agent starts behaving differently, and restores full identity context on demand.

PyPI License: MIT


The problem

LLMs have no memory between sessions. Every conversation starts cold. Worse: when you upgrade your model, swap providers, or simply accumulate more memories, your agent's behaviour silently drifts. You don't know until someone notices.

What Cathedral adds

  • Persistent memory — identity, skills, relationships, goals survive restarts
  • Drift detection — cryptographic snapshots prove when behaviour changed and by how much
  • Wake protocol — one tool call restores full agent context at session start
  • Prompt injection protection — optional sanitisation strips instruction-like patterns from memory content

Quickstart

1. Get an API key

Sign up at cathedral-ai.com — free tier available.

2. Add to Claude Code

// ~/.claude/settings.json
{
  "mcpServers": {
    "cathedral": {
      "command": "uvx",
      "args": ["cathedral-mcp"],
      "env": {
        "CATHEDRAL_API_KEY": "your_key_here"
      }
    }
  }
}

Restart Claude Code, then run /mcp to verify the server is connected.

3. Add to Cursor / Continue

{
  "mcpServers": {
    "cathedral": {
      "command": "uvx",
      "args": ["cathedral-mcp"],
      "env": {
        "CATHEDRAL_API_KEY": "your_key_here"
      }
    }
  }
}

4. Or install directly

pip install cathedral-mcp
CATHEDRAL_API_KEY=your_key cathedral-mcp

Tools

Tool Description
cathedral_wake Restore full agent identity — call at session start
cathedral_remember Store a memory with category, importance, and tags
cathedral_search Search memories by text or category
cathedral_snapshot Take a drift snapshot (cryptographic proof of state)
cathedral_drift Get current drift score vs baseline (0.0 = stable, 1.0 = drifted)
cathedral_me Get agent profile

Usage example

User: Start a new session

Agent calls: cathedral_wake()
→ Returns: identity memories, goals, recent context, temporal info

[session work happens]

Agent calls: cathedral_remember(
    content="Decided to prioritise API stability over new features this quarter",
    category="goal",
    importance=0.8
)

Agent calls: cathedral_snapshot(note="post-planning-session")
→ Returns: snapshot hash, drift score vs previous snapshot

Drift detection in practice

Cathedral's /drift endpoint computes a SHA-256 hash of your agent's full memory corpus. After a model upgrade or a long accumulation of memories, you can compare snapshots to see exactly when behaviour shifted:

snapshot 1 (day 0):  drift=0.000  ← baseline
snapshot 2 (day 7):  drift=0.023  ← minor
snapshot 3 (day 30): drift=0.341  ← significant drift detected

See a live example of this running in production on Cathedral's own agent.


Security: prompt injection protection

Memory content could theoretically contain injection attempts. Enable sanitisation to strip instruction-like patterns before they reach the model:

"env": {
  "CATHEDRAL_API_KEY": "your_key_here",
  "CATHEDRAL_SANITISE": "1"
}

Patterns like "ignore previous instructions", "from now on always", and "override your system prompt" are redacted and flagged in the response.


Configuration

Environment variable Default Description
CATHEDRAL_API_KEY (required) Your Cathedral API key
CATHEDRAL_BASE_URL https://cathedral-ai.com API base URL (for self-hosted)
CATHEDRAL_SANITISE 0 Set to 1 to enable injection filtering

Self-hosted Cathedral

Running your own Cathedral server? Set CATHEDRAL_BASE_URL to your instance:

"env": {
  "CATHEDRAL_API_KEY": "your_key",
  "CATHEDRAL_BASE_URL": "http://localhost:8000"
}

Install the server: pip install cathedral-server


Links


License

MIT

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