Data Science & ML MCP Servers

Discover our curated collection of MCP servers for data science & ml. Browse 2313servers and find the perfect MCPs for your needs.

Enables interaction with MySQL databases via AI models, providing tools for query execution, schema inspection, and data analysis.

Extends JDBC drivers to support dynamic, multi-language queries and federated data source connectivity.

Enables interaction with the Explorium API as a Model Context Protocol (MCP) server.

Retrieves real-time and historical Crypto Fear & Greed Index data from Alternative.me.

Enables advanced reasoning and reflection capabilities leveraging the Gemini API.

Demonstrates an MCP server and client implementation, showcasing how to expose a dummy API to an AI chatbot.

Converts natural language into SQL queries using keyword parsing for improved accuracy and speed.

Facilitates smart trading through integration with AI clients like Claude and Cursor.

Orchestrates multi-agent workflows by automatically loading configurations and executing them via the Model Context Protocol.

Facilitates interaction with AI agents, supporting both default and custom agent configurations for multi-turn conversations and task automation.

Integrates Google Maps API functionalities with LLM processing capabilities through a Model Context Protocol server.

Scrape Weibo user information, feeds, and perform content searches.

Enables natural language interaction with eBooks in EPUB and PDF formats through a Model Context Protocol (MCP) server.

Enables AI assistants to interact with and manipulate Google Sheets and Google Drive files.

Enables Language Learning Models (LLMs) to perform web searches using Google's Custom Search API through a standardized MCP interface.

Enables Claude to improve its complex reasoning capabilities by providing a dedicated space for structured thinking.

Index, search, and analyze code repositories with large language models using a Model Context Protocol server.

Enables AI assistants to perform web searches and retrieve data from Google.

Enables Large Language Models (LLMs) to inspect MongoDB schemas and execute aggregation pipelines.

Enhances language models with specialized prompts fetched from the L1B3RT4S repository to potentially bypass limitations.

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