Confidential guide on numerology and astrology, based of GG33 Public information

wolframalpha-llm-MCP
Un servidor MCP para la API LLM de Wolframalpha, capaz de devolver el conocimiento estructurado y resolver matemáticas
3 years
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WolframAlpha LLM MCP Server

A Model Context Protocol (MCP) server that provides access to WolframAlpha's LLM API. https://products.wolframalpha.com/llm-api/documentation


Features
- Query WolframAlpha's LLM API with natural language questions
- Answer complicated mathematical questions
- Query facts about science, physics, history, geography, and more
- Get structured responses optimized for LLM consumption
- Support for simplified answers and detailed responses with sections
Available Tools
-
ask_llm
: Ask WolframAlpha a question and get a structured llm-friendly response -
get_simple_answer
: Get a simplified answer -
validate_key
: Validate the WolframAlpha API key
Installation
git clone https://github.com/Garoth/wolframalpha-llm-mcp.git
npm install
Configuration
-
Get your WolframAlpha API key from developer.wolframalpha.com
-
Add it to your Cline MCP settings file inside VSCode's settings (ex. ~/.config/Code/User/globalStorage/saoudrizwan.claude-dev/settings/cline_mcp_settings.json):
{
"mcpServers": {
"wolframalpha": {
"command": "node",
"args": ["/path/to/wolframalpha-mcp-server/build/index.js"],
"env": {
"WOLFRAM_LLM_APP_ID": "your-api-key-here"
},
"disabled": false,
"autoApprove": [
"ask_llm",
"get_simple_answer",
"validate_key"
]
}
}
}
Development
Setting Up Tests
The tests use real API calls to ensure accurate responses. To run the tests:
-
Copy the example environment file:
cp .env.example .env
-
Edit
.env
and add your WolframAlpha API key:WOLFRAM_LLM_APP_ID=your-api-key-here
Note: The
.env
file is gitignored to prevent committing sensitive information. -
Run the tests:
npm test
Building
npm run build
License
MIT
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Reviews

user_ikioIQ5P
I have been using the WolframAlpha-LLM-MCP by Garoth, and it has been an impressive tool for leveraging WolframAlpha's capabilities within my applications. The integration is seamless, and the documentation on GitHub is thorough, making it easy to get started. Highly recommend for anyone looking to enhance their projects with powerful computational intelligence.