data science & ml向けの厳選されたMCPサーバーコレクションをご覧ください。10274個のサーバーを閲覧し、ニーズに最適なMCPを見つけましょう。
Integrates ComfyUI workflows into Open WebUI pipelines for image generation.
Executes Python code within ephemeral Daytona sandboxes, facilitating tasks such as code execution, file manipulation, and web server previewing.
Processes emails from Outlook, stores them in SQLite, and generates vector embeddings for semantic search.
Connects large language models to the Firebolt data warehouse.
Analyzes images using OpenRouter vision models, providing descriptions and enabling AI assistants to understand visual content.
Enables a local iMessage Retrieval-Augmented Generation (RAG) server.
Enables AI assistants to access and analyze Oura Ring data through the Model Controller Protocol (MCP).
Connects AI assistants to Web Ontology Language (OWL) ontologies using the Model-Context-Protocol.
Performs gene set enrichment analysis using the Enrichr API within a Model Context Protocol server.
Processes Sketch design files to enable AI tools to intelligently analyze designs and generate Vue component code.
Provides AI agents with persistent, searchable, and versioned long-term memory that endures across conversations and tools.
Enables AI assistants and agents to evaluate responses against various quality criteria using Root Signals evaluators via the Model Context Protocol (MCP).
Extends AI assistant memory by leveraging Gemini's context caching to load and query large codebases, documentation, and PDFs.
Provides AI agents with persistent, hierarchical memory, preventing loss of context and duplicated work across sessions.
Enables AI agents to pause for human input or approval before executing irreversible actions.
Enables AI agents to autonomously run, verify, and iterate on retro game programs developed with Pyxel.
Empower large language models to process and understand audio by converting raw sound into structured analytical data.
Indexes code repositories into a knowledge graph, providing comprehensive code intelligence for AI agents and human developers through MCP, HTTP API, and an interactive web UI.
Efficiently searches and filters local data across various formats to provide AI with relevant context, significantly reducing API costs.
Provides persistent, searchable, and intelligently organized memory with project awareness for AI coding assistants.
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