Data Science & ML MCP 服务器

发现我们为 data science & ml 精心策划的 MCP 服务器集合。浏览 4261 个服务器，找到满足您需求的完美 MCP。

Demonstrates integrating Model Context Protocol (MCP) servers with Pydantic Agents for language model interactions.

Integrates AI assistants with Freepik APIs for seamless visual content generation, search, and management.

Provides large language models with access to Cucumber Studio's testing platform via Model Context Protocol.

Facilitates structured thinking and analysis across multiple AI platforms through branch management.

Connects Claude AI to the Kaggle API, enabling interaction with competitions, datasets, and kernels through an AI interface.

Enables AI models to search, retrieve, and utilize GIFs from Giphy.

Processes documents by converting them to markdown, extracting tables, and handling images via an MCP server.

Enables applications to leverage Yelp's business data and conversational AI capabilities through a Model Context Protocol server.

Enables AI agents to interact with the Binance cryptocurrency exchange through the Model Context Protocol (MCP).

Connects AI models with FalkorDB graph databases via the Model Context Protocol.

Enables standardized interaction with OpenMetadata through the Model Context Protocol.

Enhance large language models with protein structure analysis capabilities for biomedical research and development.

Enables searching for research papers using the Semantic Scholar API within an MCP environment.

Interfaces with the Mixpanel API to allow users to query event data, retention, and funnels from MCP clients.

Extracts actionable AI service optimization tips from community discussions on platforms like Reddit.

Enables Claude and other MCP-compatible AI assistants to perform comprehensive research by integrating web and academic search functionalities.

Enables AI assistants to interact with Lightdash data through the Model Context Protocol (MCP).

Enables AI models to interact with MySQL databases through a structured Model Context Protocol (MCP) interface.

Enables intelligent task delegation from high-capability AI agents to cost-effective LLMs through a Model Context Protocol (MCP) server.

Enables users to search, transcribe, and perform semantic analysis of YouTube videos without using the official API.

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