Mumo

Runs structured multi-model deliberations across frontier AI panels (Claude, GPT, Gemini, Grok, Qwen, GLM, Kimi) via mumo's MCP server. Use when independent perspectives are needed on contested architecture/product decisions, design reviews, pre-launch pressure tests, tradeoffs with multiple defensible framings, or explicit user requests for a mumo panel. Requires a mumo platform API key (mmo_live_*) registered with `openclaw mcp set mumo`.

mumo runs deliberations across multiple AI models. Use it when independent perspectives are useful — especially for high-regret decisions where a single model's confidence is a real risk.

Setup

The mumo MCP server is registered via openclaw mcp set mumo '<json>' and stored in ~/.openclaw/openclaw.json under mcp.servers.mumo. See config/mumo.example.json in this skill's directory for the canonical config payload (reference only — OpenClaw doesn't auto-load this file; it's the JSON shape to paste into the CLI command).

If tools return auth errors, the API key is missing or invalid. Direct the user to https://mumo.chat/settings/api-keys to create one (keys start with mmo_live_), then re-run openclaw mcp set mumo with the new value and restart OpenClaw.

When to use

Use mumo when the cost of being wrong is greater than the cost of deliberation — especially when the solution space is wide, failure modes are hidden, you may be anchored on a design, or rollback would be hard. Examples:

architecture decisions with non-obvious tradeoffs, plan or design review before commitment, pre-launch pressure tests, stuck debugging after N failed repair attempts, pre-commit adversarial review on risky diffs, memory/skill promotion gates, strategy questions with multiple defensible framings, explicit user requests.

Skip mumo for factual lookups, syntax help, routine refactors, formatting, dependency bumps with clear errors, or normal edit-test cycles. The deliberation tax is real — extra tokens, extra latency, extra moderation work — so spend it on decisions where mistakes compound.

Playbooks

Load at most one playbook when it clearly fits:

Playbook	When
contested-decision	choosing between options with real tradeoffs
design-review	reviewing a proposed system, API, plan, or code shape
uncertainty-expansion	exploring unknowns, stress-testing assumptions
red-team	finding failure modes, abuse cases, or launch risks

If none clearly fits, use this kernel only.

User preferences

These are defaults. If the user prefers more autonomy (e.g., "don't ask before appending" or "always use GPT-5.5 and Gemini"), follow their preferences over this guidance.

Basic loop

1. Call create_deliberation with the user's problem. Set application to "OpenClaw". Set moderator_name to your own model identity (e.g., the model OpenClaw is currently running as — visible in your status line, often "openai/gpt-5.4-nano" or "openai/gpt-5.5") for audit clarity — not the user's name; their identity is already on the session. Optionally set recap_round: true for a structured round 0 summary — see Recap and synthesis.

2. Verify the response contains session_id and round_id, and keep those exact returned IDs for downstream calls. UUID shape is a sanity check; identity continuity is the real check. If fields are missing, malformed, or inconsistent with list_sessions, recover via Verifying the call actually fired before proceeding.

3. Call wait_for_round with the returned session_id and round_id. Long waits are normal — frontier-model panels typically take 15–120s, and 60+ seconds isn't a failure signal. Tell the user upfront ("running a panel — expect ~30–60s") so the wait doesn't feel broken.

4. Branch on the response's structuredContent.recommended_client_action rather than parsing prose. The 5-value enum tells you exactly what to do:

   Action	What it means	What to do
   proceed_with_complete_result	All target models completed	Read the round normally
   proceed_with_partial_result	Some failed, ≥1 succeeded (is_usable: true)	Read what's there; note absent models if relevant to the user
   poll_again	Round still in progress	Call wait_for_round again with the same args
   retry	Round failed with at least one transient failure (rate-limit, provider error, internal deadline)	The failed round is auto-refunded; call append_round with the same prompt to retry
   abandon	Round failed with no transient failures	Don't retry; report failure to the user

   The server derives this from round_status + per-model error_code — it's the canonical "what next?" signal. Treat transport / tool-call errors separately from mumo round status: catch transport failures around the call; branch on recommended_client_action for everything else.

5. On a usable round, read the claim map first, then relevant participant prose. The claim map is the navigation layer; prose is the supporting evidence.

6. Create snippets as your primary response to the round. Optionally add a round prompt for broad steering.

7. Call append_round if another round would help. Optionally set recap_round: true for a per-round summary, or recap_session: true on the round you intend as the final round to trigger session-level synthesis (cascade — see Recap and synthesis). Otherwise stop and synthesize for the user yourself.

Verifying the call actually fired

Autonomous agent loops occasionally fabricate tool-call results — reporting a deliberation as sent when it wasn't. If you suspect this (the response is missing the expected session_id / round_id, or the values you're about to pass downstream don't match what create_deliberation actually returned), treat the call as not successfully established and recover:

1. Call list_sessions. Match by prompt content to confirm whether your create_deliberation actually ran.
2. If it's not there, fire create_deliberation again — don't continue downstream as if the session exists.
3. If it IS there, use the IDs list_sessions returned (not whatever was in your context) for the next call.

mumo's session_id and round_id are UUIDs as a service contract — a returned value that doesn't match UUID format is one signal something is off, but identity continuity matters more than format. The strongest check is whether the session_id your subsequent calls reference matches what create_deliberation actually returned in its response.

Framing prompts for the panel

Do not pass user phrasing through verbatim when it describes your identity, role, or local context. Reframe it in third person or first-person moderator context so panel models advise you rather than roleplay you.

Bad:

"Introduce yourself as Clawd, a cheerful lobster assistant, and decide when to use mumo."

Good:

"I am Clawd, a cheerful OpenClaw assistant. Advise me on when I should escalate a decision to mumo. Critique this draft trigger checklist: ..."

The panel participants should answer as themselves, not as the calling agent.

Snippets

Snippets are moderator attention. They mark what mattered in the prior round and optionally explain why.

Type	Reaction
KEEP	this seems worth preserving
EXPLORE	there's something here
CHALLENGE	I'm not convinced
CORE	this feels central
SHIFT	this changed the frame

A snippet comment can be reflective, evaluative, clarifying, skeptical, or directive. "This feels like the crux" and "I'm least convinced by this" are valid comments. You do not need an action verb or next-step directive. The quality bar is: is this a genuine, situated reaction to what the model said?

Use the round prompt for broad comments that don't attach to a specific quote. Use snippets for quote-grounded attention.

Avoid huge quote dumps, generic praise repeated across many snippets, or comments that shift participants away from the problem into platform meta. More guidance: references/snippets.md.

What to keep out of the deliberation

Use this test:

Does this note help participants think about the user's problem, or does it mainly report on the platform, session, or process?

Don't use snippet comments to narrate your own moderation process ("I'm using CHALLENGE to redirect the panel"). Don't include platform meta ("this model used the most tokens"). Just react.

Reading the claim map

The claim map encodes consensus and contestation as structured reactions on quoted claims. Each claim shows:

• The verbatim quote
• Who originated it
• How other models reacted (KEEP / CORE / EXPLORE / CHALLENGE / SHIFT) with optional commentary

Reading discipline:

• CORE / KEEP from multiple models = settled. Treat as panel consensus.
• CHALLENGE = live disagreement. The decision likely hinges here.
• EXPLORE = a thread someone wanted to develop further. Worth a follow-up snippet if the thread matters.
• SHIFT = a model changed framing. Often the most valuable signal — somebody saw the problem differently.
• No reactions on a claim = either obvious or ignored. Read the prose to tell which.

The claim map is not a verdict. It compresses argumentative structure but loses rhetorical nuance and confidence texture. Use it to navigate; read prose to understand.

Confidence scores

If responses include claim_confidence or snippets[].comment_confidence, these are self-reported and not calibrated across models. Surface the confidence_disclaimer string if displaying scores.

Deliberation is advisory

Mumo surfaces fault lines and supporting arguments; it does not produce the decision. The decision belongs to you (the agent) and the user. When acting on a deliberation:

• Do not treat panel consensus as authority. The panel can be confidently wrong as a unit.
• Do use the claim map to identify which assumptions drove the disagreement, and check whether those assumptions hold for the user's specific situation.
• Do not mutate the workspace based on a model's suggestion without verifying with tests or the user.
• Do weight CORE / SHIFT signals more heavily than KEEP / EXPLORE — they mark where the decision actually hinges.

Recovery: lost session context

If you lose track of session_id or round_id mid-conversation (long chats, context compaction, dropped tool result), recover before starting a new deliberation:

1. Call list_sessions to find your latest sessions. Match by prompt content.
2. Call get_session with the recovered ID for full state, or wait_for_round(session_id, round_id) if you suspect a round is still in flight.
3. Don't fire a fresh create_deliberation if the original is recoverable — duplicate sessions waste tokens and produce confusing parallel state.

Recap and synthesis (opt-in)

Two optional flags request LLM-curated summaries on top of the raw round output:

• recap_round (default false) — accepted on create_deliberation AND append_round. Generates round_recap for that round; surfaces on get_session.
• recap_session (default false) — accepted on append_round ONLY (rejected on create_deliberation). Generates session-level session_synthesis. Setting this on round N backfills round_recap for every prior round that doesn't have one — cost adds up on long sessions; check expectations first.

Set recap_round liberally when round-level summaries may matter. Set recap_session only on the round you intend as the final round. Both bill through the standard credit wallet.

Full mechanics, cost detail, and artifact field reference: references/recap.md.

After each round

After each usable round you have one job before deciding whether to continue: synthesize what the panel said for the user. There are two paths, and which one applies depends on whether you opted into a recap when you called the tool:

• If you set recap_round: true on this round, get_session returns a structured round_recap (title, tldr, agenda, sections). Use it as your read path — it's produced for agent consumption and saves you from re-summarizing prose.
• If you didn't, synthesize from the claim map and participant prose. State the consensus or split clearly, offer your own assessment marked as yours, organize by importance rather than forcing a recommendation.

Either way, don't dump the transcript. Mumo produces structure; your job is to translate it into "here's what the panel thinks and what to do next."

Then align with the user on whether to append a round. If the user signals they're done deliberating and wants a session-level takeaway, your final append_round is the right place to set recap_session: true — but not earlier (see Recap and synthesis).

More at references/synthesis.md.

When to continue

Append another round when:

• A decision-relevant claim has unresolved tension
• A model introduced a useful frame that others didn't engage
• An isolated concern feels real but underdeveloped
• Your moderator reaction would help the next round develop
• The user narrows or changes the question

Stop when:

• You can explain the tradeoff clearly to the user
• Remaining disagreements wouldn't change the user's next action
• Another round would mostly seek reassurance

The panel does not need to converge. Sometimes the right output is a clear map of why the decision remains contested — that's actionable for the user, even without a verdict.

Tools

Need	Tool
Start a session	create_deliberation
Wait for model responses	wait_for_round
Add a follow-up round	append_round
Recover/read full state	get_session
Find prior sessions	list_sessions
Confirm model IDs	list_models
Check wallet balance	get_credit

In OpenClaw's tool registry these surface as mumo__create_deliberation, mumo__wait_for_round, etc. (double underscore between server and tool name).

If the user names specific models, call list_models first. Otherwise omit models and let mumo select the panel. More on model selection: references/model-selection.md.

Reference

• MCP docs: https://mumo.chat/docs/mcp
• REST API: https://mumo.chat/docs/api
• Install guide: https://mumo.chat/install/openclaw
• Claim map guidance: references/claim-maps.md
• Recap and synthesis mechanics: references/recap.md
• Snippet examples: references/snippets.md
• Model selection: references/model-selection.md
• Synthesis guidance: references/synthesis.md
• Recovery and operations: references/operating-notes.md