mumo MCP server

• Registry namechat.mumo/mcpverified in the MCP registry

The registry name sits under mumo.chat. The registry only lets a publisher use that namespace after proving control of the domain through DNS or HTTP.

• Stateanswerschecked 2026-10-23
• Tools8
• Server calls itselfmumo
• Server version1.1.0
• Protocol version2025-06-18

On 2026-10-23 the server answered initialize and tools/list without a sign-in. First answer in 129 ms.

• Its tools do every use case we checked: compare answers from several AI models side by side; check a travel plan against other models' suggestions.
• Checked on 4 of the 7 days it takes to judge reliability.

Said by rules from the measurements above. The likely first call is a model's pick from the tool list; read its description before relying on it.

• INITE MCP scoreafter a weeka score needs 7 daily checks: 4 so far
• What an agent can do with it2 of 2 use cases
• Answeredmeasuring4 of the 7 daily checks it takes to print a share
• Median first answer192 ms
• Space in an agent's context≈ 3,976 tokensthe definitions of all its tools (8), which an agent carries on every turn
• Tool definitionsA · 100 of 100
• Typed results1 of 8 toolsdeclared outputSchema: results come typed, read once

• Every tool passes all six checks.

A program checks the server once a day: initialize and tools/list, no model. Tokens are the definitions' length at about four characters a token. The grade averages six checks of every tool's name, description, schema and annotations; it says how well the tools are described, not whether they work. The score weighs what was measured and nothing else: answering (25%), doing the product's job (25%), definitions (20%), context (10%), use (10%) and how readily a client connects (10%). A part that was not measured is left out, and a hidden instruction caps the score at 40. The state of vendors' MCP servers: method and numbers →

• create_deliberation

  Start a multi-model AI deliberation on a question or decision. Runs the prompt through multiple frontier AI models in parallel and extracts typed reactions (KEEP/EXPLORE/CHALLENGE/CORE/SHIFT) into a claim map of where they agree and…

• append_round

  Add a follow-up round to an existing deliberation session. Use after reading the previous round's claim_map and responses to steer the conversation. The `snippets` array lets you forward specific quotes from the prior round's responses…

• wait_for_round

  Wait for a deliberation round to finish, then return the full session. This is the agent-friendly follow-up to `create_deliberation` and `append_round`: those tools return a fast ack; this tool polls the lightweight progress endpoint…

• get_session

  Fetch the full state of a deliberation session including all rounds, responses, snippets, and claim maps. Use to re-read completed session state. While a round is running, prefer `wait_for_round` so you do not repeatedly fetch the full…

• list_sessions

  List the caller's deliberation sessions, optionally filtered by status. Most useful for agents managing multiple concurrent sessions — use `status: 'ready'` to find sessions awaiting your next round. Returns a lightweight list (no…

• list_models

  List the full model catalog. Returns id, provider, display name, context window, max output tokens, pricing, and a per-model `available` verdict for your account (with `unavailable_reason` when false). Nothing is filtered out…

• share_session

  Share a deliberation session at a public URL — anyone with the link can view it in the read-only reader deck. Returns the public `share_url` plus two machine-readable twins: `markdown_url` (the full transcript with per-round claim maps…

• get_credit

  Fetch the caller's current credit wallet balance. Returns `effective_balance_usd` (markup-included, what's left to spend). At or below zero, every new round is refused until top-up or the monthly free-credit reset. Above zero, a round is…