data science & ml를 위한 엄선된 MCP 서버 컬렉션을 찾아보세요. 6549개의 서버를 탐색하고 필요에 맞는 완벽한 MCP를 찾아보세요.
Standardizes context interaction between AI models and development environments using FastAPI and the Model Context Protocol (MCP).
Enables saving, searching, and formatting memories with semantic understanding directly from Cline and Claude Desktop using a Memory Box instance.
Connects the Bear note-taking app to AI assistants, enabling semantic search and retrieval of notes.
Enables AI models to securely access and read local files using the Model Context Protocol (MCP).
Simulates quantum circuits with noise models and integrates with Model Context Protocol (MCP) clients.
Enables LLMs to inspect MySQL database schemas and execute read-only queries.
Enhance literature reviews by enabling LLMs to access and interact with academic papers.
Exposes a SQLite database as an MCP server with table schemas, read-only SQL query capabilities, and prompt templates for data analysis.
Provides access to Federal Reserve Economic Data (FRED) for Large Language Models (LLMs) like Claude.
Exposes all AKShare data interfaces as a MCP Server.
Provides example implementations for Spring AI's Model Context Protocol, demonstrating interaction with various databases.
Provides access to Reactome pathway and systems biology data through a Model Context Protocol server.
Streamlines development workflows by providing consistent environments, tooling configurations, and coding patterns for modern AI Agentic coding across multiple repositories.
Integrates with Apple Notes to create a personal memory system for AI, enabling recall and saving of information from macOS.
Indexes and provides semantic search for local documents using a vector database and local language models.
Provides access to the gnomAD GraphQL API for AI assistants.
Enables Large Language Models to explore and interact with the Fediverse using standardized Model Context Protocol tools.
Capture and organize technical details, code context, GitHub issues, and personal reflections into a Git-based project journal.
Enables AI agents to interact with Google BigQuery databases via natural language queries and schema exploration.
Enables AI agents to parse and extract information from various document types including DOCX, PDF, Excel, and TXT.
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