OpenClaw Consensus

Name: OpenClaw Consensus
Author: MICONNM

by MICONNMversion 0.1.1i★ –iRuns on your computer

Registry nameio.github.MICONNM/openclaw-consensus-mcp

Install

Overview

Description from the maker (original English)

9-LLM consensus + disagreement scoring + cheapest-route picks to fight hallucinations.

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
UsageiNot measured yet★ –

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Connection: Runs on your computer
Transport: stdio
Category: Search & AI memory
You need to fill in: RAPIDAPI_KEY

Install

Pick the AI app you use, then copy the settings.

Before you install

Basic checks: 1 of 1 passed
Runs on your computer with your user’s permissions (files, network, apps).
You fill in yourself: RAPIDAPI_KEY

Values marked secret are keys to your account. Never share them or paste them into a chat.

You need

PyPIPython’s uv installed (for the uvx command)

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "openclaw-consensus": {
      "command": "uvx",
      "args": [
        "openclaw-consensus-mcp"
      ],
      "env": {
        "RAPIDAPI_KEY": "your-value"
      }
    }
  }
}

Run this in your terminal

claude mcp add openclaw-consensus --env RAPIDAPI_KEY=your-value -- uvx openclaw-consensus-mcp

Fill in the empty values before running it.

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "openclaw-consensus": {
      "command": "uvx",
      "args": [
        "openclaw-consensus-mcp"
      ],
      "env": {
        "RAPIDAPI_KEY": "your-value"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "openclaw-consensus": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "openclaw-consensus-mcp"
      ],
      "env": {
        "RAPIDAPI_KEY": "your-value"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

Values you fill in

RAPIDAPI_KEY secret required env
Your RapidAPI key from https://rapidapi.com/yanmiayn/api/openclaw-consensus

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 7, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account MICONNM, which owns the repo
… Not checked yet
Still maintained
We have not read this repo from GitHub yet
… Not checked yet
Has a licence
We have not read this repo from GitHub yet
– Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
– Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
… Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Search & AI memoryi

More stars than 0% of the other 5662 servers in this category
Published to the registry more recently than 12% of the other 5662 servers in this category
Basic checks equal to or better than 100% of the other 5662 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is OpenClaw Consensus safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

You need to fill in: RAPIDAPI_KEY

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (0.1.1) reached the registry 6 months ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.

What can I use instead?

In the same category: Context7, Codebase Memory, Exa

Alternatives

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Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

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Install

README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

OpenClaw Consensus MCP

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

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

Values you fill in

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Search & AI memoryi

Common questions

Alternatives

Context7

Codebase Memory

Exa

README from the maker

OpenClaw Consensus MCP

What it does

Why consensus?

Install

or

Claude Desktop config

Tools

consensus(prompt, mode="balanced")

disagreement_score(prompt)

cheapest_route(prompt, target_quality=0.85)

Local development

Publish

Limitations

Security

License

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

`disagreement_score(prompt)`

`cheapest_route(prompt, target_quality=0.85)`