Data Science & ML MCP Servers

Qdrant

1,201

Connects language models to a Qdrant vector database for storing and retrieving information.

A2V

1,201

Enables AI agents to measure, exchange, and settle value on-chain within a decentralized network.

Npcpy

1,187

Facilitates the integration of AI models into daily workflows through agent-based frameworks and command-line tools.

Tavily

1,157

Integrates Tavily's search and data extraction capabilities with AI assistants via the Model Context Protocol.

MySQL

1,148

Enables LLMs to inspect MySQL database schemas and execute read-only queries.

Fast

1,126

Integrates AI models into Ruby applications via the Model Context Protocol.

AWorld

1,117

Facilitates the building, evaluation, and running of general multi-agent assistance systems.

Spring AI Examples

1,113

Provides example applications demonstrating the use of the Spring AI project.

Chatsum

1,033

Summarizes chat messages based on user queries.

Minima

1,031

Enables on-premises conversational retrieval augmented generation (RAG) with configurable containers and optional ChatGPT or Claude integration.

ROS

973

Bridges large language models and AI agents with physical robots, translating natural language commands into ROS/ROS2 instructions without code changes.

ChunkHound

968

Transforms codebases into searchable knowledge bases for AI assistants, providing both semantic and regex search capabilities via the Model Context Protocol.

Windbg Analyzer

939

Enables AI models to analyze Windows crash dumps by bridging LLMs with WinDBG/CDB.

Langcorn

938

Serves LangChain LLM applications and agents as robust FastAPI-powered API endpoints.

Agent Communication Protocol

934

Establishes an open, standardized protocol for framework-agnostic agent communication.

Pixelle

908

Converts ComfyUI workflows into MCP tools to enable multimodal AI-generated content solutions powered by LLMs.

Inkeep Agents

899

Enables the creation of collaborative AI agents using a visual builder or TypeScript SDK for solving complex problems.

Nuxt Dev

897

Facilitates AI models in comprehending the structure and context of your Vite or Nuxt applications during development.

Neo4j

886

Enables natural language interaction with Neo4j databases and Aura accounts using the Model Context Protocol.

Jupyter

880

Enables interaction with Jupyter notebooks running in a local JupyterLab environment through the Model Context Protocol (MCP).