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Cognition Wheel MCP Server

A Model Context Protocol (MCP) server that implements a "wisdom of crowds" approach to AI reasoning by consulting multiple state-of-the-art language models in parallel and synthesizing their responses.

Quick Start

# Run directly with npx (no installation needed)
npx mcp-cognition-wheel

# Or install globally
npm install -g mcp-cognition-wheel
mcp-cognition-wheel

Option 2: Build from source

  1. Clone the repository

  2. Install dependencies: pnpm install

  3. Copy .env.example to .env and add your API keys

  4. Build the project: pnpm run build

Related MCP server: AI Council MCP Server

How It Works

The Cognition Wheel follows a three-phase process:

  1. Parallel Consultation: Simultaneously queries three different AI models:

    • Claude-4-Opus (Anthropic)

    • Gemini-2.5-Pro (Google)

    • O3 (OpenAI)

  2. Anonymous Analysis: Uses code names (Alpha, Beta, Gamma) to eliminate bias during the synthesis phase

  3. Smart Synthesis: Randomly selects one of the models to act as a synthesizer, which analyzes all responses and produces a final, comprehensive answer

Features

  • Parallel Processing: All models are queried simultaneously for faster results

  • Bias Reduction: Anonymous code names prevent synthesizer bias toward specific models

  • Internet Search: Optional web search capabilities for all models

  • Detailed Logging: Comprehensive debug logs for transparency and troubleshooting

  • Robust Error Handling: Graceful degradation when individual models fail

Installation

# Run directly with npx (no installation needed)
npx mcp-cognition-wheel

# Or install globally
npm install -g mcp-cognition-wheel
mcp-cognition-wheel

Option 2: Build from source

  1. Clone the repository

  2. Install dependencies: pnpm install

  3. Copy .env.example to .env and add your API keys

  4. Build the project: pnpm run build

Usage

This is an MCP server designed to be used with MCP-compatible clients like Claude Desktop or other MCP tools.

Required Environment Variables

  • ANTHROPIC_API_KEY: Your Anthropic API key

  • GOOGLE_GENERATIVE_AI_API_KEY: Your Google AI API key

  • OPENAI_API_KEY: Your OpenAI API key

Using with Cursor

Based on the guide from this dev.to article, here's how to integrate with Cursor:

  1. Open Cursor Settings:

    • Go to Settings → MCP

    • Click "Add new MCP server"

  2. Configure the server:

    • Name: cognition-wheel

    • Command: npx

    • Args: ["-y", "mcp-cognition-wheel"]

    Example configuration:

    {
      "cognition-wheel": {
        "command": "npx",
        "args": ["-y", "mcp-cognition-wheel"],
        "env": {
          "ANTHROPIC_API_KEY": "your_anthropic_key",
          "GOOGLE_GENERATIVE_AI_API_KEY": "your_google_key", 
          "OPENAI_API_KEY": "your_openai_key"
        }
      }
    }

Option 2: Using local build

  1. Build the project (if not already done):

    pnpm run build
  2. Configure the server:

    • Name: cognition-wheel

    • Command: node

    • Args: ["/absolute/path/to/your/cognition-wheel/dist/app.js"]

    Example configuration:

    {
      "cognition-wheel": {
        "command": "node",
        "args": [
          "/Users/yourname/path/to/cognition-wheel/dist/app.js"
        ],
        "env": {
          "ANTHROPIC_API_KEY": "your_anthropic_key",
          "GOOGLE_GENERATIVE_AI_API_KEY": "your_google_key", 
          "OPENAI_API_KEY": "your_openai_key"
        }
      }
    }
  3. Test the integration:

    • Enter Agent mode in Cursor

    • Ask a complex question that would benefit from multiple AI perspectives

    • The cognition_wheel tool should be automatically triggered

Tool Usage

The server provides a single tool called cognition_wheel with the following parameters:

  • context: Background information and context for the problem

  • question: The specific question you want answered

  • enable_internet_search: Boolean flag to enable web search capabilities

Development

  • pnpm run dev: Watch mode for development

  • pnpm run build: Build the TypeScript code

  • pnpm run start: Run the server directly with tsx

Docker

Build and run with Docker:

# Build the image
docker build -t cognition-wheel .

# Run with environment variables
docker run --rm \
  -e ANTHROPIC_API_KEY=your_key \
  -e GOOGLE_GENERATIVE_AI_API_KEY=your_key \
  -e OPENAI_API_KEY=your_key \
  cognition-wheel

License

MIT

Available Tools

1 tool
cognition_wheelA

A tool that consults three AI models (Claude Opus, Gemini 2.0, GPT-4) in parallel, then uses one of them to synthesize the results into a single, high-quality answer. Use this for complex questions requiring deep analysis and verification.

ParametersJSON Schema
NameRequiredDescriptionDefault
contextYesImportant background information and context for the problem to be solved.
questionYesThe specific, detailed question you want to be answered.
enable_internet_searchYesSet to true to allow the three models to search the internet for information.

TDQS

A3.8/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Lacks annotations, so description carries full burden. Describes parallel consultation and synthesis, but omits details like latency, cost, result format, or behavior of internet search. Adequate but not thorough.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two succinct sentences, front-loaded with essential information. Every word earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

No output schema or annotations. Description does not explain return value structure, error handling, or behavior when internet search is enabled. Significant gaps for a tool with three required parameters.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema covers all parameters (100% coverage). Description adds no extra meaning beyond schema, such as how 'enable_internet_search' affects processing. Baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool consults three AI models in parallel and synthesizes results into a single answer. It uses specific verbs and identifies the resource (three distinct models), making the purpose unmistakable.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly recommends use for 'complex questions requiring deep analysis and verification,' providing clear context. No alternatives or exclusions are given, but given no siblings, this is sufficient.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. 1 tool updatev0.6.2
    • First observedcognition_wheel

TDQS

A3.8/5.0
Disambiguation5/5

Only one tool exists, leaving no room for ambiguity or confusion between tools.

Naming Consistency5/5

With a single tool, the naming is trivially consistent using snake_case.

Tool Count3/5

A single tool for this specialized purpose is acceptable but minimal; the server could benefit from additional tools for subtasks like model selection or intermediate result access.

Completeness2/5

The one tool covers the entire synthesis workflow, but there are obvious gaps, such as lack of configuration options, error handling per model, or ability to query models individually.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources

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