AI Peer Review

You run a local MCP server that lets your language models request real-time peer review feedback from Google Gemini. This setup helps you improve accuracy, clarity, and overall quality of the output while keeping control over when reviews happen and what data is shared.

How to use

Use this MCP server with any compatible local LLM client to trigger peer reviews on demand. When you want feedback, instruct your LLM to call the ai_peer_review tool to verify and improve an answer. The server handles sending the user content to Google Gemini, receiving the feedback, and returning an enhanced response that you can present to the user.

How to install

Prerequisites you need before installing the MCP server:

• Python 3.8+ installed on your system
• LMStudio or another MCP-compatible LLM client
• Google AI Studio account (free) for Gemini API access
• A local LLM with tool calling support (example: Llama 3.1, Mistral, Qwen)

Follow these concrete steps to install and run the MCP server locally:

# Clone or create project directory
# Replace with the actual repository path you've prepared
# This is a representative clone command; use your own repo URL if applicable
git clone https://github.com/your-repo/ai-peer-review-mcp
cd ai-peer-review-mcp

# Create a virtual environment (recommended)
python3 -m venv venv
source venv/bin/activate  # On Windows use `venv\Scripts\activate`

# Install dependencies
pip install -r requirements.txt

# Create environment file
cp .env.example .env
# Now, edit the .env file and add your API key:
# GEMINI_API_KEY=your_actual_api_key_here

Additional configuration and usage notes

Configuration and runtime details are provided here so you can tailor the server to your environment. The MCP server runs as a local process and is configured via a small JSON-like setup in your MCP client.

Configuration and runtime details

Environment variables you need to set for the server:

GEMINI_API_KEY=your_actual_api_key_here

Troubleshooting

If you encounter issues, check the following common areas: ensure the MCP server configuration in your client points to the correct command and path, verify that your local model supports tool calling, and restart the MCP client after any configuration changes.

Security and privacy

Content is shared with Gemini only when you explicitly trigger a peer review. No data is stored beyond the current session, and the Gemini API key remains on your machine when configured locally.

Usage examples

Basic flow: you provide a question, the LLM generates an answer, you request peer review, and the enhanced answer returns after Gemini provides feedback.

Notes on tools and endpoints

This server exposes a single tool named ai_peer_review which coordinates the peer review workflow with Gemini. Use this tool in your LLM prompts to trigger the review and receive improved responses.

Available tools

ai_peer_review

Triggers a peer review through Gemini and returns feedback to improve the LLM response.