data science & ml를 위한 엄선된 MCP 서버 컬렉션을 찾아보세요. 7327개의 서버를 탐색하고 필요에 맞는 완벽한 MCP를 찾아보세요.
Enables Large Language Models to retrieve and reference Jewish texts from the Sefaria library through a standardized interface.
Enables high-performance, scalable image generation using DALL·E 3 through a standardized Model Context Protocol (MCP) interface.
Enables AI-powered natural language interaction and management of Snowflake databases.
Connects Claude to custom SQL databases for real-time query and interaction, acting as a Model Context Protocol (MCP) desktop extension.
Offers concise, topic-wise notes on machine learning, combining mathematical foundations, practical perspectives, and architectural overviews with diagrams.
Provides reliable and durable mathematical operations by bridging Model Context Protocol (MCP) tools with Temporal's Nexus RPC framework.
Enhances and cleans raw prompts using AI, improving clarity, actionability, and effectiveness.
Integrates with Mineru API to parse documents using the Model Context Protocol, offering advanced recognition and multi-format support.
Provides persistent knowledge graph storage for AI assistants in VS Code, leveraging Azure Functions and Table storage.
Analyzes code for security risks and manages code snapshots using AI, integrating directly with Model Context Protocol-compatible AI tools.
Provides intelligent AI-driven customer support capabilities by leveraging the Model Context Protocol.
Integrate advanced optical character recognition (OCR) capabilities into large language models by leveraging the Google Cloud Vision API.
Enables Claude Code to delegate advanced AI tasks to IBM watsonx.ai foundation models like Granite, Llama, and Mistral.
Powers an AI memory system with local semantic search and embedding generation using SQLite.
Exposes Apache Lucene's full-text search capabilities through a conversational interface, enabling AI assistants to search, index, and manage document collections without technical expertise.
Integrates structured periodization with adaptive, physiology-driven decision-making for training.
Automates security operations by detecting, investigating, and responding to incidents using multi-agent AI and real MCP tool calls.
Retrieves structured, ranked evidence from the Europe PMC publication database, transforming raw literature into machine-readable evidence items for AI-driven clinical research.
Scores, compiles, and optimizes prompts for any LLM, enforcing structure and reducing token costs without internal AI calls.
Empowers AI agents with real email addresses and phone numbers, enabling seamless asynchronous communication through code recipes for various AI frameworks.
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