Montycat Mcp

Montycat MCP — Shared Memory for AI Agents

**A self-hosted MCP server that gives AI agents persistent, searchable memory.** Claude, Codex, Cursor, and any Model Context Protocol client write to one memory and read each other's.

[](https://pypi.org/project/montycat-mcp/) [](https://pypi.org/project/montycat-mcp/) [](LICENSE)

• Memory that survives the chat. Decisions, preferences, and project context carry into the next session.
• One memory, many agents. Every MCP client you use works from the same facts.
• Recall by meaning, keyword, or both. Vector search finds a memory when the wording differs, BM25 nails exact identifiers, and hybrid mode fuses the two. Exact-key and metadata lookup too.
• Yours. Server, engine, and embeddings run on your machine. No hosted memory service, no cloud embedding API.

Install

**Claude Desktop** — download the latest `.mcpb` from [Releases](https://github.com/MontyGovernance/montycat-mcp/releases/latest) and drag it into Claude Desktop. No Python needed.

**Claude Code** — install [`uv`](https://docs.astral.sh/uv/getting-started/installation/), then:

/plugin marketplace add MontyGovernance/montycat-mcp
/plugin install montycat-mcp@montygovernance

`/mcp` confirms the `montycat` server is connected.

**Codex, Cursor, other MCP clients** — point your client's stdio config at `uvx montycat-mcp` (Python 3.10+). For Codex:

codex mcp add montycat -- uvx montycat-mcp

The engine

Memory lives in a [Montycat Semantic](https://montygovernance.com) engine. Montycat MCP starts a loc