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mcp-magma-handbook

v3.0.0

Published

Enhanced MCP server with multi-query search, hybrid search, and collections for MAGMA computational algebra system

Downloads

92

Readme

🧙‍♂️ MCP MAGMA Handbook Server

npm version License: MIT

An intelligent MCP (Model Context Protocol) server that provides AI assistants with comprehensive access to the MAGMA computational algebra system handbook through advanced vector search and semantic understanding.

✨ Features

  • 🔍 Semantic Search: Natural language queries across the entire MAGMA handbook
  • 📚 Smart Examples: Retrieve code examples categorized by complexity and topic
  • 🧠 Code Explanation: Get detailed, contextual explanations of MAGMA code
  • 🏷️ Category Filtering: Search within specific categories (syntax, functions, algorithms, examples, theory)
  • Vector Database: Powered by Supabase pgvector for lightning-fast similarity search
  • 🎯 MAGMA-Optimized: Specialized parsing and understanding of MAGMA syntax and concepts

🎯 Perfect For

  • 🔬 Researchers working with computational algebra
  • 👨‍🎓 Students learning MAGMA and algebraic computation
  • 💻 Developers building mathematical software
  • 📖 Anyone needing quick access to MAGMA documentation

🚀 Quick Start

Installation

npm install -g mcp-magma-handbook

Prerequisites

  • Node.js 18+
  • OpenAI API key (for embeddings)
  • Supabase account (free tier works great!)
  • MAGMA Handbook PDF

Setup

  1. Place your MAGMA handbook PDF in the data/pdfs/ directory:

    mkdir -p data/pdfs
    cp /path/to/MAGMA_HANDBOOK.pdf data/pdfs/
  2. Index the handbook:

    npm run index
  3. Configure your MCP client (e.g., Claude Desktop):

    {
      "mcpServers": {
        "magma-handbook": {
          "command": "npx",
          "args": ["mcp-magma-handbook"],
          "env": {
            "OPENAI_API_KEY": "your-api-key-here"
          }
        }
      }
    }

Usage

Once configured, the AI assistant can use these tools:

search_magma

Search for specific topics in the MAGMA handbook:

"Search for information about elliptic curves in MAGMA"

get_magma_example

Get code examples for mathematical topics:

"Show me MAGMA examples for computing Galois groups"

explain_magma_code

Get explanations for MAGMA code:

"Explain this MAGMA code: E := EllipticCurve([GF(23) | 1, 1]);"

💬 Example Conversations

Once configured, you can ask Claude questions like:

Basic Syntax:

"How do I define a finite field in MAGMA?"

Code Examples:

"Show me examples of computing with elliptic curves over finite fields"

Code Explanation:

"Explain this MAGMA code: G := PerfectClosure(GF(8)); H := AutomorphismGroup(G);"

Advanced Topics:

"Find algorithms for computing Galois groups of polynomials"

Research Help:

"What are the available functions for working with algebraic curves in MAGMA?"

Environment Variables

  • OPENAI_API_KEY: Required for embedding generation (uses text-embedding-3-small)
  • CHROMA_SERVER_HOST: Optional, for remote ChromaDB instance
  • CHROMA_SERVER_PORT: Optional, for remote ChromaDB instance

Development

# Install dependencies
npm install

# Build TypeScript
npm run build

# Run in development mode
npm run dev

# Run tests
npm test

🛠️ Technical Details

Architecture

  • Backend: Node.js with TypeScript
  • Vector DB: Supabase with pgvector extension
  • Embeddings: OpenAI text-embedding-3-small
  • Document Processing: LangChain with optimized chunking
  • Protocol: MCP (Model Context Protocol) 1.0

Performance

  • 8,730+ indexed document chunks
  • ~175 batches for efficient processing
  • Sub-second search responses
  • Semantic similarity scoring

🤝 Contributing

We welcome contributions! Please see our Contributing Guide for details.

Development Setup

git clone https://github.com/LeGenAI/mcp-magma-handbook.git
cd mcp-magma-handbook
npm install
npm run dev

📄 License

MIT License - see LICENSE file for details.

🙏 Acknowledgments

  • MAGMA Team for the comprehensive computational algebra system
  • Anthropic for the Model Context Protocol
  • Supabase for the excellent vector database platform
  • OpenAI for powerful embedding models

Made with ❤️ for the computational algebra community