Mumo MCP vs Relay
A side-by-side look at the Mumo MCP and Relay MCP servers: transport, auth, tools, quality signals, and the config to install either one in Claude, Cursor, Windsurf or VS Code.
Verdict: choose Mumo MCP for conversational ai work over a hosted endpoint; choose Relay for conversational ai as a local process. You can also run both side by side in one mcpServers block.
Which MCP server should you choose?
Choose Mumo MCP when
- You need dedicated capabilities in the Conversational AI domain.
- You prefer a remote HTTP/SSE transport.
- Its auth model fits you: No auth required.
- You want tools like
create_deliberation, append_round, wait_for_round.
Choose Relay when
- You need dedicated capabilities in the Conversational AI domain.
- You prefer a local stdio subprocess transport.
- You have the keys it needs:
RELAY_API_KEY, RELAY_BASE_URL. - You want tools like
search_models, get_model, get_pricing.
Feature & specification comparison
Rows where the two servers differ are marked.
| Spec | ||
|---|---|---|
| Category | Conversational AI | Conversational AI |
| Quality signal | 67/100 · Great | 61/100 · Good |
| Install command | Confirmed | Confirmed |
| Transport | Remote HTTP/SSE | Local subprocess (stdio) |
| Auth | No auth required | Not declared |
| Pricing | Not declared | Not declared |
| Required env vars | None listed | RELAY_API_KEYRELAY_BASE_URL |
| Verified owner | Community listing | Community listing |
| GitHub stars | 0 | 0 |
| Directory activity | 1 views 0 installs 0 upvotes | 1 views 0 installs 0 upvotes |
| Tools | 8 | 14 |
| Full listing | View Mumo MCP | View Relay |
Tools & capabilities
Mumo MCP tools 8
create_deliberationStart 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 diverge. **Returns immediately (async).** The response is a compact ack — `{ session_id, round_id, round_index, status: 'processing', progress_url, ... }` — NOT the finished deliberation. Model execution runs in the background. Call `wait_for_round` to block on cheap progress polling and read the actual responses and claim_map once the round completes (typically 15–120s depending on model choice). `wait_for_round` returns protocol-native `structuredContent` so you can branch on `round_status` / `recommended_client_action` without parsing prose. MCP deliberations are agent-moderated: one round runs, then you decide whether to stop or call `append_round` with follow-up steering. There is no fixed round cap and no autonomous moderator on this surface. Use when you need structured disagreement from independent models, not a single synthesized answer. Ideal for spec review, architectural decisions, or any question where model diversity is a signal. Idempotency is automatic — the adapter derives a stable key from tool arguments, so repeat tool calls with identical args resolve to the same session for 24 hours (no second session, no second charge). A catastrophically-failed round (all providers errored) auto-refunds your budget within 60s; the round won't count against your daily cap.append_roundAdd 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 with a typed reaction: - KEEP: this point is strong, preserve it - EXPLORE: dig deeper on this thread - CHALLENGE: push back on this claim - CORE: this is load-bearing, build on it - SHIFT: this reframes the question Models see the snippets as curated forwards and respond with full context. Use this to resolve disagreements, test claims, or direct attention to specific passages. Forwarding is the moderator's privileged promotion of ledger content into the record — you have seen notes the panel has not. Elevate what the discussion needs now rather than waiting to see whether its author carries it. **Returns immediately (async).** Response is a compact ack — model execution runs in background. Call `wait_for_round` with this `session_id` and `round_id` from the ack to read the finished round; its `structuredContent` carries `round_status` (4-way), `is_usable`, and `recommended_client_action` so you can decide whether to read the result, retry, or abandon without parsing prose. Only works on sessions that don't have an in-flight round. If `get_session` shows the latest round is `pending`, wait. If a prior round failed catastrophically and was refunded, you can append a new round — the refund restored your budget. Idempotency is automatic per-tool-args, so retrying after a transport failure is safe — a second call with identical args within 24 hours replays the first round's ack without creating a duplicate (no second round, no second charge).wait_for_roundWait 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 internally and fetches the full transcript only once terminal state is reached. Use this instead of repeatedly calling `get_session` while models are running. The returned claim_map is your advance look at the panel's private notes — nothing in it has reached the other participants yet. Steer with it. **Web-created rounds** (started from the mumo web UI rather than this API) resolve when all model responses have settled — round artifacts (claim map, takeaway) may still be generating at that moment. `claim_map_url` is always present and does not imply the map is ready; if it matters, re-check via `get_session` shortly after. **Structured output.** The response carries both `content[0].text` (human-readable — full session markdown on terminal, short prose summary on timeout) and `structuredContent` matching the tool's `outputSchema`. Branch on `structuredContent.round_status` (`complete` | `partial_failure` | `in_progress` | `failed`) and `recommended_client_action` (`proceed_with_complete_result` | `proceed_with_partial_result` | `poll_again` | `retry` | `abandon`) rather than parsing prose. The schema also exposes per-model `completed_models[]` (with `tokens_in` / `tokens_out` / `finish_reason` / `is_partial` / `output_cap`), `in_progress_models[]` (with `partial_text_length` / `last_chunk_at` / `since_last_chunk_ms` / `expired_at_read`), and `failed_models[]` (with canonical `error_code`, `output_cap` and any preserved `partial_text`). `output_cap` is null unless a platform cap bound the call: `{ tokens, reason }` with reason `guest` (guest output limit) or `balance` (the remaining credit bounded the output — top up or wait for the reset rather than retrying). Always HTTP 200 with explicit semantic state — failed rounds don't crash the tool call.get_sessionFetch 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 transcript. The claim_map on each round is the highest-signal representation: shows which claims multiple models reacted to, who originated them, and what each reacting model said (KEEP/CHALLENGE/etc + their comment). Use claim_map to identify unresolved disagreements before sending a follow-up. Reactions are the panel's private ledger — participants never see each other's reactions, and as moderator you read them FIRST: the claim map is an advance view of what each model is privately weighing before any of it reaches prose. Use that privileged preview to steer the next round — forward a reaction as a snippet when it deserves the record immediately, or shape your prompt around a collision you can see coming. Authors weave their own strongest notes into their next responses; in later rounds you can compare a prior round's ledger against the prose that followed to spot what was dropped.list_sessionsList 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 response bodies); call `get_session` for full content.list_modelsList 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 — `available` is what varies by caller. Call before `create_deliberation` if the user wants specific models. Model IDs from this endpoint are what `create_deliberation`'s `models` parameter expects.share_sessionShare 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 — the review/audit surface) and `brief_url` (synthesis-only triage tier, ~1-2k tokens). Hand the markdown twin to a reviewer model, or the share_url to a human — no web UI required. Semantics: idempotent — re-calling returns the same URL. The link is a point-in-time snapshot that auto-refreshes when you've appended rounds since the last share, so after extending a session, call this again to bring the public page current. Sharing never changes the session itself and never lists the page in search engines or the public library (that elevation is a separate platform-curated step). Call when the user wants a shareable link or asks to publish/share results. Requires a registered (non-anonymous) mumo account; sharing generates any missing round takeaways first, so the first share of a long session can take ~15-30s.get_creditFetch 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 admitted when the balance also covers that round's minimum (each model's reserved output plus input and the post-round reactions/takeaway) — a per-request check, so a low positive balance can still refuse a large panel with `credit_exhausted`, whose `required_usd` names the minimum and `affordable_alternatives` the rosters that fit (from `create_deliberation` only; always empty from `append_round`). `per_model_minimum_usd_default` is also returned, and `pricing.minimum_usd` on `list_models` carries per-model values. Both are reported for reference — they no longer gate admission.
Relay tools 14
search_modelsLists Relay models by tag and/or text. Common tags: text-to-image, image-to-image, text-to-video, image-to-video, video-to-video, text-to-speech, speech-to-text, text-to-text, text-to-embedding. image-to-image also lists every image model that takes an input image. Returns name, tag, route and accepts (input media the model takes, e.g. ["image"]; [] for none) per model (at most 50). Then call get_model with a name for its input schema.get_modelReturns one model's route, request_schema (what run_model's input must follow), request_example, pricing and availability. Then call run_model with an input shaped like request_example.get_pricingReturns Relay's public list prices (USD), optionally for one model, plus media_storage: the per-file fees of the store_output / upload retention SKUs (relay1d, relay7d, …). Then estimate_cost for a number.estimate_costEstimates the USD cost of a call from the public price list: an estimate, not an invoice (the bill uses provider-reported usage and any custom pricing on your account). Pass only the usage fields the model's billing_type (see get_model or get_pricing) reads: per_token: input_tokens, output_tokens, cached_input_tokens; per_image: image_count, resolution_tier; per_second_video: duration_seconds, quality_mode, sound; per_second_audio: duration_seconds; per_character: character_count; per_media_token: media_input_tokens, media_output_tokens. Add store_output / retention / file_count for storage fees.run_modelRuns any Relay model by name (billed to your key). input follows get_model's request_schema (request_example runs as is). Every model whose schema takes async (image, video, audio) is submitted as a task: it waits up to 45 s, past that it returns a task_id — do not resubmit, call check_task. Chat models answer inline. Result links expire 1 h after completion unless store_output names a media_storage SKU from get_pricing. Pass an idempotency_key if you may retry the same submit. Needs a Relay credential.check_taskChecks a task run_model returned (no key needed: the task_id is the capability). Waits up to 30 s for it to finish; returns the result when completed, else its status: then call check_task again, never resubmit. Task results are kept 1 h after they finish.upload_fileHosts an image, video or audio file on Relay and returns its url: pass that url to any *_url input of run_model. Give exactly one of path (local server only, ≤ 100 MB), base64 (≤ 4 MB) or url (a public link Relay copies). retention: relay1h (default, free within a daily quota) or a media_storage SKU from get_pricing. Needs a Relay credential.list_workflowsList Relay's workflow templates: multi-step pipelines (e.g. script → voiceover) run as one call. Each entry has workflow_id, input_schema and its steps (step_id + model). Run one with run_workflow; inputs follow input_schema.run_workflowStart a workflow run (one submit) and wait up to wait_seconds (≤ 45) for it to finish. Completed → the final output; still running → run_id: call check_workflow_run with it, never resubmit (a new run bills again). Each step is billed as an ordinary request. webhook_url gets ONE signed workflow.completed / workflow.failed delivery when the run ends. Get workflow_id and the inputs shape from list_workflows. Needs a Relay credential.check_workflow_runPoll a workflow run by run_id, waiting up to wait_seconds (≤ 30) for it to end. Completed → the final output; failed / cancelled → the error; still running → its steps so far: call check_workflow_run again. No key needed: the run_id is the access token.cancel_workflow_runCancel a workflow run. Cancels between steps only: a step already running completes and bills. A run that has already ended answers WORKFLOW_RUN_NOT_CANCELLABLE. Needs a Relay credential.get_creditsRead the account's credit balance, active promo credits and consumption. Needs a dashboard JWT or a custom-tier superkey; a plain inference key gets PERMISSION_DENIED (403). Needs a Relay credential.- +2 more on the listing page
Ready-to-paste client configs
Paste either block (or both) into your client config, e.g. claude_desktop_config.json or ~/.cursor/mcp.json. We only show a config when the install command is confirmed.
Mumo MCP
{
"mcpServers": {
"mcp-46": {
"url": "https://mumo.chat/api/mcp"
}
}
}
Relay
{
"mcpServers": {
"relay-4": {
"command": "npx",
"args": [
"-y",
"@relaygpu/mcp"
],
"env": {
"RELAY_API_KEY": "YOUR_RELAY_API_KEY_HERE",
"RELAY_BASE_URL": "YOUR_RELAY_BASE_URL_HERE"
}
}
}
}
Frequently asked questions
Mumo MCP is categorized under Conversational AI and uses a remote HTTP/SSE transport. Relay belongs to Conversational AI and uses a local stdio subprocess. Pick Mumo MCP for conversational ai work and Relay for conversational ai.
Copy the mcpServers block from the config section on this page into your client's config (for example claude_desktop_config.json or ~/.cursor/mcp.json), then fully restart the client.
Mumo MCP has no declared pricing — check the project's own site. Auth: no auth required. Relay has no declared pricing — check the project's own site. If a server needs environment variables such as API keys, define them under the env key in your client's MCP config.
Yes. MCP clients support multiple servers: add mcp-46 and relay-4 as separate keys in the same mcpServers object, and your assistant will call tools from either one.
On AllMCPs, Mumo MCP has a quality signal of 67/100 (Great) with 1 views, 0 installs and 0 GitHub stars. Relay has 61/100 (Good) with 1 views, 0 installs and 0 GitHub stars.