An extensible TypeScript-based MCP server designed for Claude Code with a modular architecture for easily adding custom tools. It includes built-in examples like a calculator and echo tool, utilizing Zod for robust input validation and error handling.
A Model Context Protocol server built with FastMCP that enables dynamic tool loading and configuration from individual Python files. It provides a flexible framework for automatically discovering, testing, and running tools via Stdio or HTTP transport modes.
A development server that automatically discovers and exposes tools from the src/tools directory, allowing you to create and run Machine Comprehension Protocol (MCP) services with either HTTP or STDIO transport methods.
A server that leverages the XMCP framework to discover and execute tools from the src/tools directory, supporting both SSE and STDIO transport methods for interaction.
Enable Claude (or any other LLM) to interactively debug your code (set breakpoints and evaluate expressions in stack frame).
It's language-agnostic, assuming debugger console support and valid launch.json for debugging in VSCode.
A Model Context Protocol (MCP) server template designed for building structured tools, prompts, and resources with built-in support for HTTP and STDIO transports. It provides a standardized framework for developers to create and deploy AI-driven services using TypeScript and Zod schema validation.
A Model Context Protocol server built with FastMCP that features dynamic tool loading and modular management via a dedicated tool directory. It supports both stdio and HTTP transport modes, enabling efficient development and deployment of custom MCP tools.
An MCP server framework featuring dynamic tool loading and a modular one-tool-per-file architecture for rapid development. It supports both Stdio and HTTP transport modes, offering automated test generation and centralized configuration management.
A Python template for building Model Context Protocol (MCP) servers that expose tools via JSON-RPC, enabling secure and scalable context and tool invocation for language models.
Enables Windows users to connect Antigravity CLI as an MCP subagent for coding tasks such as file reading, code execution, edits, and terminal commands, with model selection, conversation continuity, and streaming progress.
MCP server that gives AI agents isolated, named Linux computers with stable IDs and lifecycle control. Supports multiple backends like Docker, Fly Machines, Modal, and E2B, with a dashboard for managing sandboxes.
A Python server implementing the Model Context Protocol to provide customizable prompt templates, resources, and tools that enhance LLM interactions in the continue.dev environment.
This MCP server integrates AI assistants with Unity Editor, allowing them to create scenes, generate scripts, simulate input, and automate workflows using 91 built-in tools.