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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

### Option 1: Use with npx (Recommended)

```bash
# 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`

## 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

### Option 1: Use with npx (Recommended)

```bash
# 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](https://dev.to/andyrewlee/use-your-own-mcp-on-cursor-in-5-minutes-1ag4), here's how to integrate with Cursor:

#### Option 1: Using npx (Recommended)

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:
   ```json
   {
     "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):
   ```bash
   pnpm run build
   ```

2. **Configure the server**:
   - **Name**: `cognition-wheel`
   - **Command**: `node`
   - **Args**: `["/absolute/path/to/your/cognition-wheel/dist/app.js"]`
   
   Example configuration:
   ```json
   {
     "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:

```bash
# 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 

TDQS

A3.8/5.0

Scored across 1 tool

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