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Montycat MCP - Shared Memory for AI Agents MCP server

by MontyGovernance·io.github.MontyGovernance/montycat-mcp·v1.1.5
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Shared, persistent memory for AI agents, with semantic recall and live updates.

A85/100grade A
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A85/100

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Montycat MCP - Shared Memory for AI Agents tools (24, 5 write)

write = sends, deletes, buys or posts

Read from the package source without running it. The installed server may list more.

  • montycat_await_memory_change

    Wait until memory CHANGES — returns the moment another agent or session writes, updates, or deletes something in this memory.

  • montycat_clean_snapshotswrite action

    Delete snapshot files for one existing in-memory keyspace.

  • montycat_create_keyspacewrite action

    Create a new memory namespace using the configured owner's authority.

  • montycat_disable_semantic

    Disable semantic search for one explicit keyspace.

  • montycat_enable_external_vectors

    Enroll a keyspace for caller-supplied embeddings instead of text embedding.

  • montycat_enable_semantic

    Enable semantic search for one explicit keyspace.

  • montycat_forgetwrite action

    Delete a stored record from memory by key or custom key.

  • montycat_install_engine

    Install the Montycat engine on THIS computer, then start it.

  • montycat_list_enforced_schemas

    List schemas enforced on a keyspace, including field data types.

  • montycat_list_keyspaces

    List the available memory stores and keyspaces on this Montycat engine.

  • montycat_list_memories

    Browse stored memories — enumerate what is remembered, not search by meaning.

  • montycat_policy_explain

    Explain whether the configured owner may perform a proposed action.

  • montycat_policy_history

    View governance history visible to the configured owner.

  • montycat_policy_view

    View the configured owner's effective Montycat governance policy.

  • montycat_recall

    Recall memory by exact key or by field filter (not by meaning).

  • montycat_reembed_semantic

    Replace an enrolled keyspace's text embedding model and backfill it.

  • montycat_remember

    Store a fact or record in memory; it is embedded and indexed automatically.

  • montycat_remember_bulk

    Store many memories at once; all are embedded and indexed automatically.

  • montycat_remove_keyspacewrite action

    Permanently remove a memory namespace using the owner's authority.

  • montycat_semantic_search
  • montycat_semantic_status

    Read the engine's actual semantic configuration and backfill state.

  • montycat_start_snapshots

    Start scheduled snapshots for one existing in-memory keyspace.

  • montycat_stop_snapshots

    Stop scheduled snapshots for one existing in-memory keyspace.

  • montycat_updatewrite action

    Revise an existing memory in place (memory is mutable).

Public scan report

scanner v0.1.5 · 2026-09-19 · same rubric, same numbers if you re-run it

no findings
  • Code scan10 source files scanned25/25
  • –Live reliabilityno gateway calls yet and no remote to proben/a
  • –Tool poisoningtools not inspected (local package is not executed); not countedn/a
  • Auth qualitystatic API keys via environment variables6/15
  • Maintenancelast push 0 days ago15/15
  • Maintainer identityregistry namespace matches repository owner; GitHub account older than a year9/10
Overall 85/100. Components that don't apply are left out of the denominator. Any critical finding is an F.RubricAppeal a findingJSON

What the publisher says

From the Montycat MCP - Shared Memory for AI Agents repository's README, as published. We do not edit it. Read it on GitHub

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.

<!-- mcp-name: io.github.MontyGovernance/montycat-mcp -->

  • 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 montycat-mcp.mcpb and drag it into Claude Desktop. No Python needed. That link always serves the current release; every release also carries a version-named copy and a .sha256 to check it against.

Claude Code — install uv, 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 engine. Montycat MCP starts a local one for you, so most people can stop reading here.

Point it at an engine you already run:

export MONTYCAT_URI="montycat://memory-agent:password@localhost:21210/memories"
export MONTYCAT_TLS=true   # remote engines only

Or start one yourself with Docker:

docker run -d --name montycat -p 21210:21210 -p 21211:21211 \
  -e MONTYCAT_SUPEROWNER=admin -e MONTYCAT_PASSWORD=change-me \
  -v montycat_data:/var/lib/.montycat \
  montygovernance/montycat:semantic

On Apple Silicon use the arm64-semantic tag instead — semantic is the amd64 image, and it crashes under emulation. Port 21211 carries live memory watches.

Use it

Talk to your agent normally; it picks the tool.

Remember that the team chose PostgreSQL for the billing service.

What did we decide about the billing database?

Save this to the shared engineering scope.

scope decides where a memory lives — alice for private, engineering for a team, shared for common. It is a namespace, not a security boundary: for real isolation, give each MCP server its own least-privilege Montycat credential.

Tools

Shortened. The full README is on GitHub.

Nothing above is checked by us. What we check is on the safety report.

Install directly

claude mcp add montycat-mcp -- uvx montycat-mcp
Add to Cursor

Montycat MCP - Shared Memory for AI Agents: common questions

Is Montycat MCP - Shared Memory for AI Agents MCP server safe?
Yes, by our scan: it is graded A (85/100). Read the Montycat MCP - Shared Memory for AI Agents safety report
How do I install Montycat MCP - Shared Memory for AI Agents?
It runs on your machine. Copy the Claude Code, Claude Desktop or Cursor config from the install section.
Does Montycat MCP - Shared Memory for AI Agents need an API key?
Yes. The registry entry asks for MONTYCAT_URI.
Is Montycat MCP - Shared Memory for AI Agents maintained?
The last commit was 2 days ago (2026-09-19). The latest release is v1.1.5.
What can I use instead of Montycat MCP - Shared Memory for AI Agents?
Servers from other publishers that do the same job: State Memory MCP server, Knowl MCP server and Plur MCP server. Compare all Montycat MCP - Shared Memory for AI Agents alternatives.

Alternatives to Montycat MCP - Shared Memory for AI Agents

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