JupyterMCP integrates Jupyter Notebook (version 6.x) with Claude AI using the Model Context Protocol, enabling real-time interaction between AI models and live notebooks. It facilitates two-way communication, allowing Claude AI to execute code, manipulate cells, and retrieve outputs directly within the notebook. The system comprises a WebSocket server, client-side JavaScript, and an MCP server for seamless protocol implementation. Designed for developers and data scientists, it brings advanced AI assistance into interactive notebook environments.

• ⭐ 111
• MCP
• jjsantos01/jupyter-notebook-mcp

Label Studio MCP Server implements a Model Context Protocol (MCP) interface to enable programmatic management of Label Studio projects, tasks, and model predictions. It enables structured interactions with a running Label Studio instance via MCP clients, supports various project and task operations, and integrates the official Label Studio SDK. The tool exposes multiple functions for project management, task handling, and prediction integration, allowing both natural language and structured API calls.

• ⭐ 23
• MCP
• HumanSignal/label-studio-mcp-server

GrowthBook MCP Server enables seamless integration between GrowthBook and LLM clients by implementing the Model Context Protocol. It allows users to view experiment details, add feature flags, and manage GrowthBook configurations directly from AI applications. The server is configurable via environment variables and leverages GrowthBook's API for functionality. This integration streamlines experimentation and feature management workflows in AI tools.

• ⭐ 15
• MCP
• growthbook/growthbook-mcp

Jupyter MCP Server is an implementation of the Model Context Protocol (MCP) designed to enable AI-driven, real-time management and interaction with Jupyter Notebooks. It offers context-aware capabilities, smart execution features, and multimodal output handling, seamlessly integrating with JupyterLab and supporting multiple notebooks simultaneously. The server is compatible with any MCP client and can work with local or hosted Jupyter deployments.

• ⭐ 765
• MCP
• datalayer/jupyter-mcp-server

Chronulus MCP provides an MCP server enabling Claude users to interact with Chronulus AI forecasting and prediction agents. It allows seamless integration through multiple methods including pip, docker, and uvx, offering flexibility in deployment. The tool is designed to fit into the Claude desktop client's model context protocol configuration, streamlining the usage of Chronulus AI’s predictive capabilities in various workflows.

• ⭐ 103
• MCP
• ChronulusAI/chronulus-mcp

Calculator Server is a Go-based implementation of the Model Context Protocol (MCP) designed to offer comprehensive mathematical computation services. It features 13 advanced tools including support for arithmetic, scientific, statistical, unit conversion, and financial calculations. The server provides high-precision operations, bulk processing for unit conversions, and sophisticated financial modeling capabilities, making it suitable for both scientific and financial applications.

• ⭐ 0
• MCP
• avisangle/calculator-server

DoorDash MCP Server enables seamless integration between the DoorDash API and Model Context Protocol (MCP) compatible clients such as Claude Desktop, Windsurf, and Cursor. It provides an easy setup process to compile and build the server using TypeScript and Node.js, offering users a JSON configuration to connect with MCP clients. The server supports API key configuration for secure access. Direct support and feedback channels are available for users requiring assistance.

• ⭐ 14
• MCP
• JordanDalton/DoorDash-MCP-Server

Kaggle MCP Server implements the Model Context Protocol (MCP) using the fastmcp library and provides tools for searching and downloading datasets from Kaggle via a standardized MCP interface. It manages Kaggle API authentication, exposes search and download tools as MCP resources, and offers prompts for generating exploratory data analysis notebooks. The server can be run locally or via Docker, supporting easy integration with MCP clients and compliant applications.

• ⭐ 28
• MCP
• arrismo/kaggle-mcp

AutoMobile provides a comprehensive set of tools for mobile automation, focusing on UI testing and development workflow automation. It operates as an MCP Server, enabling a robust interaction loop for model-driven actions and observations. The solution supports Android platforms with features like automated test authoring, multi-device management, and seamless CI test execution. AutoMobile also offers source mapping and deep view hierarchy analysis to enhance code rendering accuracy.

• ⭐ 63
• MCP
• zillow/auto-mobile

Implements a Model Context Protocol (MCP) server for interfacing with the ZenML API, enabling standardized access to ZenML resources for AI applications. Provides tools for reading data about users, stacks, pipelines, runs, and artifacts, as well as triggering new pipeline runs if templates are available. Includes robust testing, automated quality checks, and supports secure connection from compatible MCP clients. Designed for easy integration with ZenML instances, supporting both local and remote ZenML deployments.

• ⭐ 32
• MCP
• zenml-io/mcp-zenml

MCP Swagger Server converts OpenAPI/Swagger API specifications into Model Context Protocol (MCP) compatible tools, enabling REST APIs to become directly callable by AI systems. It supports zero-configuration conversion, multiple transport protocols (SSE, Streamable, Stdio), and secure API access through Bearer Token authentication. The tool offers an interactive command-line interface and configuration options to filter operations, customize transports, and manage API security. Its modular structure includes OpenAPI parsing, web UI, and backend services.

• ⭐ 38
• MCP
• zaizaizhao/mcp-swagger-server

MCP-Typescribe is an open-source implementation of the Model Context Protocol (MCP) focused on providing LLMs with contextual, real-time access to TypeScript API documentation. It parses TypeScript (and other) definitions using TypeDoc-generated JSON and serves this information via a queryable server that supports tools used by AI coding assistants. The solution enables AI agents to dynamically explore, search, and understand unknown APIs, accelerating onboarding and supporting agentic behaviors in code generation.

• ⭐ 45
• MCP
• yWorks/mcp-typescribe

ROS MCP is a server that implements the Model Context Protocol (MCP) to enable natural language control of robots in ROS environments. It facilitates communication with ROS topics, services, and actions, supporting any ROS message type. The system integrates with GUI tools through a socket server and can be used with Claude Desktop for interactive robot management. Key functionalities include topic management, node control, service interaction, and process management for ROS2-powered robots.

• ⭐ 26
• MCP
• Yutarop/ros-mcp

Multi-Model Advisor is a Model Context Protocol (MCP) server that queries several Ollama AI models, each with customizable roles, and combines their responses to deliver diverse perspectives on a single question. It allows users to assign unique system prompts to each model and integrate seamlessly with tools like Claude for Desktop. Configuration is flexible via environment variables, supporting easy setup and model management. The tool is designed to provide a 'council of advisors' experience by synthesizing opinions from multiple local models.

• ⭐ 67
• MCP
• YuChenSSR/multi-ai-advisor-mcp

Mindmap MCP Server provides a Model Context Protocol (MCP) compatible service that transforms Markdown input into interactive mindmap visualizations. It supports command-line, Python, and Docker installation methods for flexibility across platforms. Designed to integrate with MCP clients like Claude Desktop, it ensures seamless Markdown-to-mindmap conversion using markmap under the hood. The server is intended for easy plug-and-play use in model-powered workflows that require structured, visual context formatting.

• ⭐ 205
• MCP
• YuChenSSR/mindmap-mcp-server