OpenClaw Consensus MCP

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Multi-model consensus inside MCP clients: compare answers, surface disagreement, and escalate only when needed.

OpenClaw Consensus MCP wraps the OpenClaw Consensus API as three Model Context Protocol tools. It is designed for workflows where a maintainer wants a second opinion before accepting a risky answer, review summary, or routing decision.

What it does

OpenClaw runs the same prompt across multiple models, then returns:

a consensus answer with confidence and model response metadata,
a disagreement heuristic derived from the deep consensus response, and
a cheapest route recommendation that tries smaller model sets before escalating.

This MCP server exposes those three capabilities as tools so Claude Desktop / Claude Code can call them mid-conversation.

Why consensus?

A single model can produce a confident but incorrect answer. Comparing multiple responses does not prove correctness, but disagreement is a useful signal that a maintainer should review the output more carefully.

Install

pip install openclaw-consensus-mcp
# or
uv pip install openclaw-consensus-mcp

You also need a RapidAPI key for the OpenClaw Consensus API: <https://rapidapi.com/yanmiayn/api/openclaw-consensus>

Set it in your environment:

export RAPIDAPI_KEY="your-rapidapi-key"

Claude Desktop config

Add to ~/.claude/claude_desktop_config.json (macOS/Linux) or %APPDATA%\Claude\claude_desktop_config.json (Windows):

{
  "mcpServers": {
    "openclaw-consensus": {
      "command": "openclaw-consensus",
      "env": {
        "RAPIDAPI_KEY": "your-rapidapi-key"
      }
    }
  }
}

For Claude Code:

claude mcp add openclaw-consensus -- openclaw-consensus

Tools

`consensus(prompt, mode="balanced")`

Get a 9-LLM consensus answer.

prompt (string) — the question.
mode (string, default balanced) — deep (9 models), balanced (5), or fast (3).

Returns

{
  "consensus": "string",
  "confidence": 0.0,
  "models_responded": 5,
  "votes": []
}

The consensus tool returns the upstream API response as-is. Fields may expand as the endpoint evolves.

`disagreement_score(prompt)`

How much the deep consensus response disagrees on a prompt.

Returns

{
  "disagreement": 0.0,
  "confidence": 1.0,
  "models_responded": 9,
  "votes": []
}

`cheapest_route(prompt, target_quality=0.85)`

Try fast, balanced, and deep modes in order until the confidence threshold is met.

Returns

{
  "selected_mode": "balanced",
  "models_used": 5,
  "confidence": 0.9,
  "answer": "string"
}

Local development

git clone https://github.com/MICONNM/openclaw-consensus-mcp
cd openclaw-consensus-mcp
uv venv && source .venv/bin/activate
uv pip install -e ".[dev]"
pytest

Smoke-test the server with the official MCP Inspector:

npx @modelcontextprotocol/inspector openclaw-consensus

Publish

uv build
uv publish      # to PyPI
mcp-publisher publish   # to the official MCP Registry

See CONTRIBUTING.md for the development workflow and docs/maintainer-workflow.md for triage, review, security, and release responsibilities.

Limitations

Consensus is a review aid, not a correctness guarantee.
Network-backed tools require a configured OpenClaw endpoint and may incur provider charges.
Do not send secrets, private source code, or personal data unless your endpoint policy explicitly allows it.

Security

Please report vulnerabilities privately using the process in SECURITY.md.

License

MIT — see LICENSE.

OpenClaw Consensus