Elrond MCP - Thinking Augmentation Server

A Model Context Protocol (MCP) server that provides hierarchical LLM critique and synthesis for enhanced decision-making and idea evaluation.

Overview

Elrond MCP implements a multi-agent thinking augmentation system that analyzes proposals through three specialized critique perspectives (positive, neutral, negative) and synthesizes them into comprehensive, actionable insights. This approach helps overcome single-model biases and provides more thorough analysis of complex ideas.

Features

• Parallel Critique Analysis: Three specialized agents analyze proposals simultaneously from different perspectives

• Structured Responses: Uses Pydantic models and instructor library for reliable, structured outputs

• Google AI Integration: Leverages Gemini 2.5 Flash for critiques and Gemini 2.5 Pro for synthesis

• MCP Compliance: Full Model Context Protocol support for seamless integration with AI assistants

• Comprehensive Analysis: Covers feasibility, risks, benefits, implementation, stakeholder impact, and resource requirements

• Consensus Building: Identifies areas of agreement and disagreement across perspectives

Architecture

Installation

Prerequisites

• Python 3.13 or higher

• Google AI API key (get one at Google AI Studio)

Setup

1. Clone the repository:

   git clone <repository-url> cd elrond-mcp

2. Install dependencies:

   # Using uv (recommended) uv sync --dev --all-extras # Or using pip pip install -e .[dev]

3. Configure API key:

   export GEMINI_API_KEY="your-gemini-api-key-here" # Or create a .env file echo "GEMINI_API_KEY=your-gemini-api-key-here" > .env

Usage

Running the Server

Development Mode

Production Mode

Integration with Claude Desktop

1. Install for Claude Desktop:

   mcp install elrond_mcp/server.py --name "Elrond Thinking Augmentation"

2. Manual Configuration: Add to your Claude Desktop MCP settings:

   { "elrond-mcp": { "command": "python", "args": ["/path/to/elrond-mcp/main.py"], "env": { "GEMINI_API_KEY": "your-api-key-here" } } }

Using the Tools

Augment Thinking Tool

Analyze any proposal through multi-perspective critique:

Check System Status Tool

Monitor the health and configuration of the thinking augmentation system:

Response Structure

Critique Response

Each critique agent provides:

• Executive Summary: Brief overview of the perspective

• Structured Analysis:

  • Feasibility assessment

  • Risk identification

  • Benefit analysis

  • Implementation considerations

  • Stakeholder impact

  • Resource requirements

• Key Insights: 3-5 critical observations

• Confidence Level: Numerical confidence (0.0-1.0)

Synthesis Response

The synthesis agent provides:

• Executive Summary: High-level recommendation

• Consensus View:

  • Areas of agreement

  • Areas of disagreement

  • Balanced assessment

  • Critical considerations

• Recommendation: Overall guidance

• Next Steps: Concrete action items

• Uncertainty Flags: Areas needing more information

• Overall Confidence: Synthesis confidence level

Development

Project Structure

Running Tests

Code Formatting

Development Script

For convenience, use the provided script to run all quality checks:

This script will:

• Sync dependencies

• Run Ruff linter with auto-fix

• Format code with Ruff

• Execute the full test suite

• Perform final lint check

• Provide a pre-commit checklist

Configuration

Environment Variables

• GEMINI_API_KEY: Required Google AI API key

• LOG_LEVEL: Logging level (default: INFO)

Model Configuration

• Critique Agents: gemini-2.5-flash

• Synthesis Agent: gemini-2.5-pro

Models can be customized by modifying the agent initialization in agents.py.

Troubleshooting

Common Issues

1. API Key Not Found

   Error: Google AI API key is required

   Solution: Set the GEMINI_API_KEY environment variable

2. Empty Proposal Error

   Error: Proposal cannot be empty

   Solution: Ensure your proposal is at least 10 characters long

3. Model Rate Limits

   Error: Rate limit exceeded

   Solution: Wait a moment and retry, or check your Google AI quota

4. Validation Errors

   ValidationError: ...

   Solution: The LLM response didn't match expected structure. This is usually temporary - retry the request

Debugging

Enable debug logging:

Check system status:

Contributing

1. Fork the repository

2. Create a feature branch

3. Make your changes

4. Add tests for new functionality

5. Run the test suite

6. Submit a pull request

License

See LICENSE

Support

For issues and questions:

• Check the troubleshooting section above

• Review the logs for detailed error information

• Open an issue on the repository

Roadmap

• Support for additional LLM providers (OpenAI, Anthropic)

• Custom critique perspectives and personas

• Performance optimization and caching

• Advanced synthesis algorithms