A simple note storage system with tools for adding notes and generating scripts from them.
A MCP server project
The server implements a simple note storage system with:
The server provides a single prompt:
The server implements two tools:
[TODO: Add configuration details specific to your implementation]
On MacOS: ~/Library/Application\ Support/Claude/claude_desktop_config.json
On Windows: %APPDATA%/Claude/claude_desktop_config.json
To prepare the package for distribution:
uv sync
uv build
This will create source and wheel distributions in the dist/
directory.
uv publish
Note: You'll need to set PyPI credentials via environment variables or command flags:
--token
or UV_PUBLISH_TOKEN
--username
/UV_PUBLISH_USERNAME
and --password
/UV_PUBLISH_PASSWORD
Since MCP servers run over stdio, debugging can be challenging. For the best debugging experience, we strongly recommend using the MCP Inspector.
You can launch the MCP Inspector via npm
with this command:
npx @modelcontextprotocol/inspector uv --directory C:\Users\INDIA\Desktop\mcp\script_generator_server run script-generator-server
Upon launching, the Inspector will display a URL that you can access in your browser to begin debugging.
A GDB/MI protocol server based on the MCP protocol, providing remote application debugging capabilities with AI assistants.
Up-to-date Docs For Any Cursor Prompt
A test server that demonstrates all features of the MCP protocol, including prompts, tools, resources, and sampling.
Generate visualizations from fetched data using the VegaLite format and renderer.
A Node.js MCP server example for the OpenWeather API, requiring an API key.
Connects Blender to Claude AI via the Model Context Protocol (MCP), enabling direct interaction and control for prompt-assisted 3D modeling, scene creation, and manipulation.
Run Python in a code sandbox.
Fetches API information from Feishu OpenAPI for seamless integration and management within an IDE.
Integration with QA Sphere test management system, enabling LLMs to discover, summarize, and interact with test cases directly from AI-powered IDEs
A Python server providing Retrieval-Augmented Generation (RAG) functionality. It indexes various document formats and requires a PostgreSQL database with pgvector.