发现我们为 data science & ml 精心策划的 MCP 服务器集合。浏览 10274 个服务器,找到满足您需求的完美 MCP。
Provides a persistent, high-performance memory system for Model Context Protocol (MCP) using libSQL.
Generates responses based on the latest information using the Gemini API and Google Search.
Generates images from text descriptions using Amazon Bedrock's Nova Canvas model.
Implements a knowledge graph with semantic search capabilities, leveraging the Qdrant vector database for persistence.
Enables standardized interaction with OpenMetadata through the Model Context Protocol.
Connects MCP-compatible clients to Onyx AI knowledge bases, enabling semantic search and chat capabilities.
Enables web scraping and crawling capabilities for Large Language Models.
Implements a Model Context Protocol server for querying and analyzing healthcare data stored in the OMOP Common Data Model.
Enables AI assistants to leverage human input via Amazon Mechanical Turk.
Enables AI video, image, and audio generation from Kling AI directly within Claude.
Transforms your Readwise library into a self-hosted, semantically searchable database for highlights.
Provides a reliable interface for AI assistants to access and query the EBI Ontology Lookup Service (OLS) for accurate ontological information.
Enables read-only access to iMessage data and macOS contacts via a Model Context Protocol (MCP) server for integration with other applications like Large Language Models.
Provides persistent memory and context recall for AI conversations and models, enhancing their ability to remember past interactions and decisions.
Empowers Large Language Models to access and analyze comprehensive App Store Optimization data from the Astro application.
Provides advanced chess analysis by implementing a Chess Context Protocol Server and integrating Stockfish, positional theme analysis, and opening databases.
Empowers large language models with a persistent, human-like memory module that integrates seamlessly outside the model itself.
Connects AI to Commodore 64 Ultimate and Ultimate 64 via a REST API using the Model Context Protocol, enabling LLM agents to control the retro computer.
Provides AI coding agents with structured code understanding through tree-sitter, significantly reducing token consumption.
Provides stable, reusable, and observable code context infrastructure for AI coding agents through hybrid retrieval, project memory, and retrieval observability.
Scroll for more results...