data science & ml를 위한 엄선된 MCP 서버 컬렉션을 찾아보세요. 7327개의 서버를 탐색하고 필요에 맞는 완벽한 MCP를 찾아보세요.
Manages and queries knowledge about codebases using vector embeddings.
Enhances AI model reasoning by implementing structured, retrieval-augmented thinking processes.
Analyzes music audio from local files, YouTube links, or audio URLs using librosa, Whisper, and LLMs.
Manages and serves standardized prompt templates for content analysis via the Model Context Protocol (MCP).
Provides comprehensive access to Chinese stock market data through a unified API.
Retrieves League of Legends data for AI assistants via natural language queries using the Riot Games API.
Manages knowledge base searches by automating workflow orchestration and intent recognition.
Enables AI assistants to query and analyze distributed tracing data from Grafana Tempo through the Model Context Protocol.
Provides an MCP server to access UK parliamentary data and enhance research with semantic search capabilities.
Provides programmatic control over MuseScore, enabling external applications and AI assistants to manipulate musical scores.
Provides a self-hostable server for automatic license plate recognition via REST and Model Context Protocol APIs.
Analyzes Java class files, decompiles them, scans Maven dependencies, and extracts method lists to enhance Large Language Models' code analysis capabilities.
Implement a comprehensive, scalable machine learning inference architecture on Amazon EKS for deploying Large Language Models (LLMs) with agentic AI capabilities, including Retrieval Augmented Generation (RAG) and intelligent document processing.
Provides a hyper-personal, always-on, open-source AI companion for automating personal and professional tasks.
Provides comprehensive access to Chinese stock market data, offering historical, real-time, financial, and news information.
Process video and audio files by converting, compressing, trimming, and extracting media with FFmpeg through natural language commands.
Enables AI agents to perform deep runtime debugging of live Java applications using the Java Debug Interface (JDI).
Provides AI assistants with deep, structured understanding of codebases, enabling offline search for definitions, reference tracing, and Git history exploration.
Generates an always-fresh, function-level code dependency graph with sub-second incremental rebuilds, supporting local semantic search and impact analysis for AI agents.
Extracts concepts from documents, measures their grounding strength, preserves disagreement, and traces everything back to its original source within a semantic knowledge graph.
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