cognition-wheel
Integrates with Google's Gemini-2.5-Pro model as part of a multi-model reasoning system that queries multiple AI models in parallel and synthesizes their responses.
Integrates with OpenAI's O3 model as part of a multi-model reasoning system that queries multiple AI models in parallel and synthesizes their responses.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@cognition-wheelWhat are the best strategies for climate change mitigation?"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
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)
# Run directly with npx (no installation needed)
npx mcp-cognition-wheel
# Or install globally
npm install -g mcp-cognition-wheel
mcp-cognition-wheelOption 2: Build from source
Clone the repository
Install dependencies:
pnpm installCopy
.env.exampleto.envand add your API keysBuild the project:
pnpm run build
Related MCP server: AI Council MCP Server
How It Works
The Cognition Wheel follows a three-phase process:
Parallel Consultation: Simultaneously queries three different AI models:
Claude-4-Opus (Anthropic)
Gemini-2.5-Pro (Google)
O3 (OpenAI)
Anonymous Analysis: Uses code names (Alpha, Beta, Gamma) to eliminate bias during the synthesis phase
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)
# Run directly with npx (no installation needed)
npx mcp-cognition-wheel
# Or install globally
npm install -g mcp-cognition-wheel
mcp-cognition-wheelOption 2: Build from source
Clone the repository
Install dependencies:
pnpm installCopy
.env.exampleto.envand add your API keysBuild 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 keyGOOGLE_GENERATIVE_AI_API_KEY: Your Google AI API keyOPENAI_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:
Option 1: Using npx (Recommended)
Open Cursor Settings:
Go to Settings → MCP
Click "Add new MCP server"
Configure the server:
Name:
cognition-wheelCommand:
npxArgs:
["-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
Build the project (if not already done):
pnpm run buildConfigure the server:
Name:
cognition-wheelCommand:
nodeArgs:
["/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" } } }Test the integration:
Enter Agent mode in Cursor
Ask a complex question that would benefit from multiple AI perspectives
The
cognition_wheeltool 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 problemquestion: The specific question you want answeredenable_internet_search: Boolean flag to enable web search capabilities
Development
pnpm run dev: Watch mode for developmentpnpm run build: Build the TypeScript codepnpm 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-wheelLicense
MIT
Available Tools
1 toolcognition_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.
| Name | Required | Description | Default |
|---|---|---|---|
| context | Yes | Important background information and context for the problem to be solved. | |
| question | Yes | The specific, detailed question you want to be answered. | |
| enable_internet_search | Yes | Set to true to allow the three models to search the internet for information. |
TDQS
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.
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.
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.
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.
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.
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 tool update
v0.6.2- First observed
cognition_wheel
TDQS
Only one tool exists, leaving no room for ambiguity or confusion between tools.
With a single tool, the naming is trivially consistent using snake_case.
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.
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
Resources
Unclaimed servers have limited discoverability.
Looking for Admin?
If you are the server author, to access and configure the admin panel.
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