Information
# MCP Wolfram Alpha (Client + Server)
Seamlessly integrate Wolfram Alpha into your chat applications.
This project implements an MCP (Model Context Protocol) server designed to interface with the Wolfram Alpha API. It enables chat-based applications to perform computational queries and retrieve structured knowledge, facilitating advanced conversational capabilities.
Included is an MCP-Client example utilizing Gemini via LangChain, demonstrating how to connect large language models to the MCP server for real-time interactions with Wolfram Alpha’s knowledge engine.
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## Features
- **Wolfram|Alpha Integration** for math, science, and data queries.
- **Modular Architecture** Easily extendable to support additional APIs and functionalities.
- **Multi-Client Support** Seamlessly handle interactions from multiple clients or interfaces.
- **MCP-Client example** using Gemini (via LangChain).
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## Installation
### Clone the Repo
\`\`\`bash
git clone https://github.com/ricocf/mcp-wolframalpha.git
cd mcp-wolframalpha
\`\`\`
### Set Up Environment Variables
Create a .env file based on the example:
- WOLFRAM_API_KEY=your_wolframalpha_appid
- GeminiAPI=your_google_gemini_api_key *(Optional if using Client method below.)*
### Install Requirements
\`\`\`bash
pip install -r requirements.txt
\`\`\`
### Configuration
To use with the VSCode MCP Server:
1. Create a configuration file at \`.vscode/mcp.json\` in your project root.
2. Use the example provided in \`configs/vscode_mcp.json\` as a template.
3. For more details, refer to the [VSCode MCP Server Guide](https://sebastian-petrus.medium.com/vscode-mcp-server-42286eed3ee7).
To use with Claude Desktop:
\`\`\`json
\{
"mcpServers": \{
"WolframAlphaServer": \{
"command": "python3",
"args": [
"/path/to/src/core/server.py"
]
\}
\}
\}
\`\`\`
## Client Usage Example
This project includes an LLM client that communicates with the MCP server.
#### Run as CLI Tool
- Required: GeminiAPI
- To run the client directly from the command line:
\`\`\`bash
python main.py
\`\`\`
#### Docker
To build and run the client inside a Docker container:
\`\`\`bash
docker build -t wolframalpha -f .devops/llm.Dockerfile .
docker run -it wolframalpha
\`\`\`