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。7261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。
Downloads YouTube subtitles using yt-dlp and connects them to claude.ai via Model Context Protocol.
Provides access to football statistics and live match data from the API-Football service.
Fetches relevant content from Supavec using the Model Context Protocol.
Manages workflows and Docker image building for Bio-OS instance platforms.
Benchmarks vLLM endpoints interactively through MCP, enabling performance evaluation of large language models.
Enables AI systems to generate and edit images using text prompts via the 4o-image API.
Leverages Monte Carlo Tree Search and Bayesian analysis to provide AI-assisted reasoning and deep exploration of topics.
Analyze real-time data and generate AI-powered insights using natural language queries.
Retrieves data from Google Analytics 4 to provide insights into website and app performance.
Generates AI-powered animations by transforming static design files into dynamic SVG components.
Empowers AI agents with advanced meta-cognition and dynamic, reflective problem-solving capabilities.
Enables high-performance, lock-free clipboard access for AI assistants, specifically bridging Windows clipboard content to WSL2 environments.
Assists students in preparing for any exam or test by serving as an intelligent, AI-powered study companion.
Exposes Go project insights and static analysis to large language models for enhanced understanding and code generation.
Provides chart generation capabilities to AI agents through a standardized Model Context Protocol interface, enabling seamless interaction with Quick Chart.
Enables AI models to search and extract data from Excel files on a local PC, converting raw data to JSON format.
Enables artificial intelligence models to perform detailed, attributed searches across a vast collection of U.S. Government datasets.
Provides comprehensive system information and management capabilities for Linux servers through MCP and HTTP REST APIs, enabling AI agents to monitor and interact with server resources.
Enables production-ready, parallel corpus analysis for AI-powered applications through a memory-mapped architecture.
Establishes local-first persistent memory for AI agents, enabling them to store, recall, and consolidate knowledge across interactions without cloud dependencies.
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