Top MCPs by Category

Appwrite MCP server is a Model Context Protocol server that enables AI models to interact with Appwrite’s backend. It provides a curated set of MCP tools to manage databases, users, functions, teams, and more within your Appwrite project, enabling powerful AI-assisted workflows and natural-language interactions with your backend. The server ships with the Databases tools enabled by default to keep prompts within context limits and can be extended by enabling additional APIs via command-line flags. This makes it easier to build AI-powered applications that leverage Appwrite APIs securely and efficiently.

Appwrite MCP server is a Model Context Protocol server that enables AI models to interact with Appwrite’s backend. It provides a curated set of MCP tools to manage databases, users, functions, teams, and more within your Appwrite project, enabling powerful AI-assisted workflows and natural-language interactions with your backend. The server ships with the Databases tools enabled by default to keep prompts within context limits and can be extended by enabling additional APIs via command-line flags. This makes it easier to build AI-powered applications that leverage Appwrite APIs securely and efficiently.

Appwrite MCP server is a Model Context Protocol server that enables AI models to interact with Appwrite’s backend. It provides a curated set of MCP tools to manage databases, users, functions, teams, and more within your Appwrite project, enabling powerful AI-assisted workflows and natural-language interactions with your backend. The server ships with the Databases tools enabled by default to keep prompts within context limits and can be extended by enabling additional APIs via command-line flags. This makes it easier to build AI-powered applications that leverage Appwrite APIs securely and efficiently.

Appwrite MCP server is a Model Context Protocol server that enables AI models to interact with Appwrite’s backend. It provides a curated set of MCP tools to manage databases, users, functions, teams, and more within your Appwrite project, enabling powerful AI-assisted workflows and natural-language interactions with your backend. The server ships with the Databases tools enabled by default to keep prompts within context limits and can be extended by enabling additional APIs via command-line flags. This makes it easier to build AI-powered applications that leverage Appwrite APIs securely and efficiently.

GitHub's official MCP Server. This repository hosts the MCP server implementation that enables Model Context Protocol (MCP) tooling for GitHub data and workflows. It exposes a wide registry of MCP tools spanning code management, repository operations, issues, pull requests, workflows, gists, and more. The documentation and commit history reveal a broad set of tools (GetMe, GetTeams, ListIssues, CreateOrUpdateFile, GetRepositoryTree, and many others) that are designed to be wired into dynamic toolsets and accessed via a consistent ServerTool pattern. This MCP server is built with extensibility in mind, supporting features like tool dependencies, dynamic toolsets, and feature flags to adapt to varied prompts and use cases. The project emphasizes a registry-driven approach where tools, resources, and prompts are defined and validated, enabling robust integration with client apps and AI models.

GitHub's official MCP Server. This repository hosts the MCP server implementation that enables Model Context Protocol (MCP) tooling for GitHub data and workflows. It exposes a wide registry of MCP tools spanning code management, repository operations, issues, pull requests, workflows, gists, and more. The documentation and commit history reveal a broad set of tools (GetMe, GetTeams, ListIssues, CreateOrUpdateFile, GetRepositoryTree, and many others) that are designed to be wired into dynamic toolsets and accessed via a consistent ServerTool pattern. This MCP server is built with extensibility in mind, supporting features like tool dependencies, dynamic toolsets, and feature flags to adapt to varied prompts and use cases. The project emphasizes a registry-driven approach where tools, resources, and prompts are defined and validated, enabling robust integration with client apps and AI models.

GitHub's official MCP Server. This repository hosts the MCP server implementation that enables Model Context Protocol (MCP) tooling for GitHub data and workflows. It exposes a wide registry of MCP tools spanning code management, repository operations, issues, pull requests, workflows, gists, and more. The documentation and commit history reveal a broad set of tools (GetMe, GetTeams, ListIssues, CreateOrUpdateFile, GetRepositoryTree, and many others) that are designed to be wired into dynamic toolsets and accessed via a consistent ServerTool pattern. This MCP server is built with extensibility in mind, supporting features like tool dependencies, dynamic toolsets, and feature flags to adapt to varied prompts and use cases. The project emphasizes a registry-driven approach where tools, resources, and prompts are defined and validated, enabling robust integration with client apps and AI models.

GitHub's official MCP Server. This repository hosts the MCP server implementation that enables Model Context Protocol (MCP) tooling for GitHub data and workflows. It exposes a wide registry of MCP tools spanning code management, repository operations, issues, pull requests, workflows, gists, and more. The documentation and commit history reveal a broad set of tools (GetMe, GetTeams, ListIssues, CreateOrUpdateFile, GetRepositoryTree, and many others) that are designed to be wired into dynamic toolsets and accessed via a consistent ServerTool pattern. This MCP server is built with extensibility in mind, supporting features like tool dependencies, dynamic toolsets, and feature flags to adapt to varied prompts and use cases. The project emphasizes a registry-driven approach where tools, resources, and prompts are defined and validated, enabling robust integration with client apps and AI models.

IO Aerospace MCP Server is a production-ready Model Context Protocol (MCP) server designed for aerospace and astrodynamics calculations. It exposes a rich set of tools for celestial body ephemeris, orbital mechanics, DSN ground station operations, solar system object properties, and comprehensive unit and time system conversions. Built on the IO Aerospace Astrodynamics framework, this server delivers core algorithms for ephemerides, geometry, and time systems, enabling developers to integrate advanced aerospace calculations into local or hosted deployments. The project supports both modern streamable-HTTP transport and legacy SSE/STDIO configurations, with self-hosting options via Docker or native .NET deployments for flexible integration into existing ecosystems.

IO Aerospace MCP Server is a production-ready Model Context Protocol (MCP) server designed for aerospace and astrodynamics calculations. It exposes a rich set of tools for celestial body ephemeris, orbital mechanics, DSN ground station operations, solar system object properties, and comprehensive unit and time system conversions. Built on the IO Aerospace Astrodynamics framework, this server delivers core algorithms for ephemerides, geometry, and time systems, enabling developers to integrate advanced aerospace calculations into local or hosted deployments. The project supports both modern streamable-HTTP transport and legacy SSE/STDIO configurations, with self-hosting options via Docker or native .NET deployments for flexible integration into existing ecosystems.

IO Aerospace MCP Server is a production-ready Model Context Protocol (MCP) server designed for aerospace and astrodynamics calculations. It exposes a rich set of tools for celestial body ephemeris, orbital mechanics, DSN ground station operations, solar system object properties, and comprehensive unit and time system conversions. Built on the IO Aerospace Astrodynamics framework, this server delivers core algorithms for ephemerides, geometry, and time systems, enabling developers to integrate advanced aerospace calculations into local or hosted deployments. The project supports both modern streamable-HTTP transport and legacy SSE/STDIO configurations, with self-hosting options via Docker or native .NET deployments for flexible integration into existing ecosystems.

IO Aerospace MCP Server is a production-ready Model Context Protocol (MCP) server designed for aerospace and astrodynamics calculations. It exposes a rich set of tools for celestial body ephemeris, orbital mechanics, DSN ground station operations, solar system object properties, and comprehensive unit and time system conversions. Built on the IO Aerospace Astrodynamics framework, this server delivers core algorithms for ephemerides, geometry, and time systems, enabling developers to integrate advanced aerospace calculations into local or hosted deployments. The project supports both modern streamable-HTTP transport and legacy SSE/STDIO configurations, with self-hosting options via Docker or native .NET deployments for flexible integration into existing ecosystems.

IO Aerospace MCP Server is a production-ready Model Context Protocol (MCP) server designed for aerospace and astrodynamics calculations. It exposes a rich set of tools for celestial body ephemeris, orbital mechanics, DSN ground station operations, solar system object properties, and comprehensive unit and time system conversions. Built on the IO Aerospace Astrodynamics framework, this server delivers core algorithms for ephemerides, geometry, and time systems, enabling developers to integrate advanced aerospace calculations into local or hosted deployments. The project supports both modern streamable-HTTP transport and legacy SSE/STDIO configurations, with self-hosting options via Docker or native .NET deployments for flexible integration into existing ecosystems.

IO Aerospace MCP Server is a production-ready Model Context Protocol (MCP) server designed for aerospace and astrodynamics calculations. It exposes a rich set of tools for celestial body ephemeris, orbital mechanics, DSN ground station operations, solar system object properties, and comprehensive unit and time system conversions. Built on the IO Aerospace Astrodynamics framework, this server delivers core algorithms for ephemerides, geometry, and time systems, enabling developers to integrate advanced aerospace calculations into local or hosted deployments. The project supports both modern streamable-HTTP transport and legacy SSE/STDIO configurations, with self-hosting options via Docker or native .NET deployments for flexible integration into existing ecosystems.

IO Aerospace MCP Server is a production-ready Model Context Protocol (MCP) server designed for aerospace and astrodynamics calculations. It exposes a rich set of tools for celestial body ephemeris, orbital mechanics, DSN ground station operations, solar system object properties, and comprehensive unit and time system conversions. Built on the IO Aerospace Astrodynamics framework, this server delivers core algorithms for ephemerides, geometry, and time systems, enabling developers to integrate advanced aerospace calculations into local or hosted deployments. The project supports both modern streamable-HTTP transport and legacy SSE/STDIO configurations, with self-hosting options via Docker or native .NET deployments for flexible integration into existing ecosystems.

IO Aerospace MCP Server is a production-ready Model Context Protocol (MCP) server designed for aerospace and astrodynamics calculations. It exposes a rich set of tools for celestial body ephemeris, orbital mechanics, DSN ground station operations, solar system object properties, and comprehensive unit and time system conversions. Built on the IO Aerospace Astrodynamics framework, this server delivers core algorithms for ephemerides, geometry, and time systems, enabling developers to integrate advanced aerospace calculations into local or hosted deployments. The project supports both modern streamable-HTTP transport and legacy SSE/STDIO configurations, with self-hosting options via Docker or native .NET deployments for flexible integration into existing ecosystems.

PersonalizationMCP is a unified personal data hub built on MCP (Model Context Protocol) that enables AI assistants to access and reason over data from Steam, YouTube, Bilibili, Spotify, Reddit, and more. This repository showcases a Python-based MCP server that aggregates platform APIs, manages OAuth2 tokens, and exposes a rich set of tools to query user data, playlists, watch history, and social actions. It emphasizes local data handling, token management automation, and a modular architecture that makes it easy to add new platforms through the @mcp.tool() decorator and server integration. Ideal for developers building context-aware assistants who want a single, extensible backend to surface personal data across multiple services. The MCP server is designed to run locally on your machine with secure configuration, offering multiple installation paths (conda, uv, or pip with virtualenv). It includes a comprehensive set of available tools organized by platform, robust token management (notably YouTube), and practical guidance for configuration, testing, and cursor-based integration with consumer apps like Cursor. The project also provides detailed setup steps for each platform, including how to obtain API keys, cookies, and OAuth credentials, ensuring a smooth path from zero to a functioning personal data hub.

PersonalizationMCP is a unified personal data hub built on MCP (Model Context Protocol) that enables AI assistants to access and reason over data from Steam, YouTube, Bilibili, Spotify, Reddit, and more. This repository showcases a Python-based MCP server that aggregates platform APIs, manages OAuth2 tokens, and exposes a rich set of tools to query user data, playlists, watch history, and social actions. It emphasizes local data handling, token management automation, and a modular architecture that makes it easy to add new platforms through the @mcp.tool() decorator and server integration. Ideal for developers building context-aware assistants who want a single, extensible backend to surface personal data across multiple services. The MCP server is designed to run locally on your machine with secure configuration, offering multiple installation paths (conda, uv, or pip with virtualenv). It includes a comprehensive set of available tools organized by platform, robust token management (notably YouTube), and practical guidance for configuration, testing, and cursor-based integration with consumer apps like Cursor. The project also provides detailed setup steps for each platform, including how to obtain API keys, cookies, and OAuth credentials, ensuring a smooth path from zero to a functioning personal data hub.

PersonalizationMCP is a unified personal data hub built on MCP (Model Context Protocol) that enables AI assistants to access and reason over data from Steam, YouTube, Bilibili, Spotify, Reddit, and more. This repository showcases a Python-based MCP server that aggregates platform APIs, manages OAuth2 tokens, and exposes a rich set of tools to query user data, playlists, watch history, and social actions. It emphasizes local data handling, token management automation, and a modular architecture that makes it easy to add new platforms through the @mcp.tool() decorator and server integration. Ideal for developers building context-aware assistants who want a single, extensible backend to surface personal data across multiple services. The MCP server is designed to run locally on your machine with secure configuration, offering multiple installation paths (conda, uv, or pip with virtualenv). It includes a comprehensive set of available tools organized by platform, robust token management (notably YouTube), and practical guidance for configuration, testing, and cursor-based integration with consumer apps like Cursor. The project also provides detailed setup steps for each platform, including how to obtain API keys, cookies, and OAuth credentials, ensuring a smooth path from zero to a functioning personal data hub.

PersonalizationMCP is a unified personal data hub built on MCP (Model Context Protocol) that enables AI assistants to access and reason over data from Steam, YouTube, Bilibili, Spotify, Reddit, and more. This repository showcases a Python-based MCP server that aggregates platform APIs, manages OAuth2 tokens, and exposes a rich set of tools to query user data, playlists, watch history, and social actions. It emphasizes local data handling, token management automation, and a modular architecture that makes it easy to add new platforms through the @mcp.tool() decorator and server integration. Ideal for developers building context-aware assistants who want a single, extensible backend to surface personal data across multiple services. The MCP server is designed to run locally on your machine with secure configuration, offering multiple installation paths (conda, uv, or pip with virtualenv). It includes a comprehensive set of available tools organized by platform, robust token management (notably YouTube), and practical guidance for configuration, testing, and cursor-based integration with consumer apps like Cursor. The project also provides detailed setup steps for each platform, including how to obtain API keys, cookies, and OAuth credentials, ensuring a smooth path from zero to a functioning personal data hub.