Discover our curated collection of MCP servers for data science & ml. Browse 10275 servers and find the perfect MCPs for your needs.
Integrates Neo4j graph databases with Claude Desktop, enabling natural language interactions.
Decomposes complex problems into independent atomic units of thought, enabling robust reasoning and validated insights.
Enables conversational exploration of codebases by providing an LLM with tools to intelligently search code.
Provides a complete walkthrough for building a server to serve a trained Random Forest model and integrating it with Bee Framework for ReAct interactivity.
Enables querying of a hybrid graph and vector database using the Model Context Protocol for enhanced document retrieval.
Enables natural language flight searches using Google's Gemini 2.5 Pro and the Model Context Protocol.
Provides a basic implementation of an MCP server and client for tools, resources, and prompts.
Bridges AI agents into robotics by implementing Model Context Protocol (MCP) for ROS2.
Provides a lightweight, local RAG memory store for Model Context Protocol (MCP) agents.
Explore real-world projects and advanced implementations of agentic AI systems, multi-agent frameworks, RAG pipelines, and AI workflow automation.
Enables interaction with text-to-speech and video translation APIs for AI-powered voice solutions.
Generates images using the Volcengine Jimeng AI API, accessible via a standardized Model Context Protocol (MCP) service.
Provides a comprehensive multimodal AI toolkit, integrating powerful capabilities from Zhipu GLM and Pollinations.AI for advanced media analysis and generation.
Empowers AI agents to orchestrate industry-standard reverse engineering tools like Ghidra, Radare2, and YARA for automated malware analysis and security research.
Provides a modern, systems-oriented scripting language that transpiles to Rust, optimized for data science and scientific computing.
Orchestrates multi-round AI debates among various language models to generate diverse viewpoints and synthesized outputs.
Accelerates AI coding agents by providing a structural map of codebases, eliminating the need to read entire source files.
Equips AI coding agents with production-ready capabilities for content generation, application building, product deployment, and real-world workflow automation.
Manages and queries large-scale graph-vector datasets with high performance, supporting OpenCypher, vector search, and advanced graph algorithms.
Automatically optimizes token usage for Claude Code and MCP workflows by compressing structured data and source code.
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