Python-based Model Context Protocol server (and sample client) that connects chat-apps or LLMs to the Wolfram Alpha API, with optional Gemini/LangChain UI.
https://github.com/akalaric/mcp-wolframalphaStop building custom Wolfram Alpha integrations from scratch. This MCP server gives your chat applications instant access to computational intelligence – from complex mathematics to real-world data queries – through a clean, standardized protocol.
You're building AI applications that need to go beyond text generation. When users ask "What's the derivative of x²sin(x)?" or "How many calories in 2 slices of pizza?", your LLM needs computational backup. Building a custom Wolfram Alpha integration means dealing with API authentication, response parsing, error handling, and rate limiting – easily 2-3 days of development work.
This MCP server handles all of that. You get enterprise-grade computational capabilities in your chat apps without the integration headache.
Immediate Integration: Works with Claude Desktop, VSCode MCP, or any MCP-compatible application. Add computational power to your existing setup in minutes, not days.
Production-Ready Architecture: Clean separation between server and client components. The server handles Wolfram Alpha API calls, while your application focuses on user experience.
Multiple Deployment Options: Run locally during development, containerize for production, or use the included Gradio UI for quick testing and demos.
Real-Time Computational Queries: Your chat applications can now solve calculus problems, analyze data sets, convert units, lookup scientific constants, and access Wolfram's vast knowledge base seamlessly.
AI-Powered Educational Tools: Students ask your chatbot math questions and get step-by-step solutions with graphs and explanations. Instead of generic "I can't do calculations" responses, you provide actual computational results.
Technical Documentation Assistants: Your internal tools can now verify calculations, convert units in technical specs, and provide accurate scientific data. When someone asks about the tensile strength of steel or optimal gear ratios, your assistant delivers precise answers.
Business Intelligence Applications: Combine natural language queries with computational analysis. "What's the compound interest on $10,000 at 3.5% over 5 years?" gets calculated instantly rather than redirecting users to external calculators.
Development Prototypes: Testing computational features in your AI applications becomes trivial. Spin up the Gradio interface, test queries, verify responses, then integrate the MCP server into your production stack.
The server runs independently and connects through the Model Context Protocol standard. Your existing chat applications, whether using Claude, custom LLMs, or other AI systems, can immediately tap into Wolfram Alpha's computational engine.
For VSCode users, add the configuration to your MCP settings and your development environment gains computational intelligence. For production applications, deploy the Docker container and point your MCP client to the server endpoint.
The included client example with Gemini integration shows exactly how to structure the communication patterns. You can adapt this approach for any LLM or chat framework.
Clone the repository and install dependencies with pip install -r requirements.txt. Add your Wolfram Alpha API key to the environment variables. The server starts with a simple Python command, and you can test computational queries immediately through the included Gradio interface.
For production deployment, the Docker configuration handles containerization and environment management automatically.
This MCP server transforms your chat applications from text-only systems into computational powerhouses. Instead of spending days building custom integrations, you get production-ready Wolfram Alpha capabilities in under an hour.