mumo

mumo — multi-model deliberation over MCP

Your agent sends a question to models from different labs — Claude, GPT, Gemini, Grok, DeepSeek, Kimi, and more — and gets back their full responses plus typed cross-model reactions. The participating models react to each other directly and explain, in their own words, what they agree with, challenge, or want to explore further. Built for architecture, plan/spec review, strategy, and pre-launch pressure tests. Free monthly credit; API key required.

The server is remote. There is nothing to run locally.

https://mumo.chat/api/mcp

Install

One-click, per host — each page walks through the key and the client's own install flow:

Any MCP client that supports Streamable HTTP with a custom header:

{

"mcpServers": { "mumo": { "url": "https://mumo.chat/api/mcp", "headers": { "Authorization": "Bearer mmo_live_YOUR_KEY_HERE" } } } }

Get a key at mumo.chat/settings/api-keys (sign-in required; keys start with mmo_live_). initialize and tools/list work without a key, so your client can inspect the tools before you create one; tool calls need the header.

Tools

Full reference, request and response shapes, and per-client notes: https://mumo.chat/docs/mcp. Machine-readable descriptors: /.well-known/mcp.json (manifest) and /api/mcp/server-card (MCP Server Card). Registry name: chat.mumo/mcp.

The loop

1. create_deliberation with the question, written in the operator's first person ("I'm deciding whether to…").
2. wait_for_round — panels take 15–120 s; a long wait is normal.
3. Read the claim map before the prose. Steer with append_round and snippets, or stop.

The panel is advisory. Read the disagreement; don't defer to whichever side has more votes.

This repo: the client baseline

Beyond the server, mumo ships a skill for each host that teaches the agent when to convene a panel and how to read one. This repo is the source of truth for that skill: one tokenized SKILL.template.md plus a per-client overlay, rendered into each sibling client repo by the build script in scripts/. Never edit a client's SKILL.md directly — edit here and re-render.

Contents

skills/mumo/

├── SKILL.template.md # tokenized baseline (the source of truth) ├── playbooks/ # shared cognitive-shape playbooks └── references/ # shared reference docs (claim maps, snippets, takeaway, etc.) scripts/ ├── build-skill.js # renderer: template + per-client overlay -> client SKILL.md ├── README.md # how the build system works └── clients/ # per-client configs + Setup/Frontmatter partials server.json # Official MCP Registry descriptor (chat.mumo/mcp)

The template uses {{TOKEN}} markers for per-client substitution points (application name, moderator example, install URL, tool-naming registry note, etc.). Each scripts/clients/ .json fills them in.

Building

node scripts/build-skill.js                      # render to all six sibling client repos

node scripts/build-skill.js --target # render one node scripts/build-skill.js --verify-all # check for drift across all six (use in CI)

See scripts/README.md for details (token reference, adding a new client, etc.).

Editing

• Shared kernel changes → edit skills/mumo/SKILL.template.md. Re-render to propagate.
• Per-client overlay changes (## Setup body, frontmatter, application name, moderator example, install URL, tool-naming note) → edit scripts/clients/ / partials or scripts/clients/ .json tokens. Re-render.
• Shared playbooks / references → edit skills/mumo/playbooks/ or skills/mumo/references/. Currently propagated manually; build-system handling is a follow-up.

Architecture

The shared-sections + per-client-overlay model is documented in docs/MCP_CLIENTS.md in the main mumo repo. The 2026-05-20 audit at docs/audits/2026-05-20-mcp-skill-delta.md enumerates exactly which sections are shared vs. per-client.

Links

• Product — https://mumo.chat
• For agents — https://mumo.chat/for-agents
• MCP reference — https://mumo.chat/docs/mcp
• REST API — https://mumo.chat/docs/api · OpenAPI — https://mumo.chat/openapi.json
• Issues — https://github.com/mumo-chat/mumo-mcp/issues

License

MIT