Gradio MCP Test
A Python-based MCP server that provides tools to get cat images, either as a direct PNG or a URL for Markdown display.
Setup:
- Create a virtualenv with
python -m venv venv - Activate it
source venv/bin/activate - Install requirements
pip install -r requirements - Run the server
python app.py
Edit MCP.json:
{
"mcpServers": {
"gradio-mcp-test": {
"url": "http://localhost:7860/gradio_api/mcp/sse"
}
}
}
The server has two tools. cat_image which will return a PNG image of a cat and cat_url that only return the url and ask the LLM to display it using Markdown.
相關伺服器
Scout Monitoring MCP
贊助Put performance and error data directly in the hands of your AI assistant.
Alpha Vantage MCP Server
贊助Access financial market data: realtime & historical stock, ETF, options, forex, crypto, commodities, fundamentals, technical indicators, & more
Tripo MCP Server
Generate 3D models with Tripo AI. Requires the external Tripo AI Blender Addon.
PsiAnimator-MCP
A server for quantum physics simulation and animation, using QuTip for computations and Manim for visualizations.
Lighthouse MCP Server
Audit web performance, accessibility, and SEO using Google Lighthouse.
Atlas Docs
Access technical documentation for libraries and frameworks, formatted in clean markdown for LLM consumption.
MCP Cat PSQL
An example of a remote, authentication-free MCP server deployable on Cloudflare Workers.
Storyblok MCP Server
Manage your Storyblok CMS using natural language through AI tools.
DevRev MCP Server
Access DevRev's APIs to manage work items, parts, search, and user information.
QGIS
connects QGIS Desktop to Claude AI through the MCP. This integration enables prompt-assisted project creation, layer loading, code execution, and more.
MCPR
Expose R functions through the Model Context Protocol (MCP) for seamless integration with AI assistants.
ndlovu-code-reviewer
Manual code reviews are time-consuming and often miss the opportunity to combine static analysis with contextual, human-friendly feedback. This project was created to experiment with MCP tooling that gives AI assistants access to a purpose-built reviewer. Uses the Gemini cli application to process the reviews at this time and linting only for typescript/javascript apps at the moment. Will add API based calls to LLM's in the future and expand linting abilities. It's also cheaper than using coderabbit ;)