This document provides Python-specific best practices and examples for implementing MCP servers using the MCP Python SDK. It covers server setup, tool registration patterns, input validation with Pydantic, error handling, and complete working examples. Use it to ground design choices in named patterns, trade-offs and examples.
This document provides Node/TypeScript-specific best practices and examples for implementing MCP servers using the MCP TypeScript SDK. It covers project structure, server setup, tool registration patterns, input validation with Zod, error handling, and complete working examples. Use it to ground design choices in named patterns, trade-offs and examples.
The name should be general, descriptive of the service being integrated, easy to infer from the task description, and without version numbers. Use it to ground design choices in named patterns, trade-offs and examples.
This document provides guidance on creating comprehensive evaluations for MCP servers. Evaluations test whether LLMs can effectively use your MCP server to answer realistic, complex questions using only the tools provided. Use it to navigate the topic and choose relevant methods, papers or tools.
Create MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. The quality of an MCP server is measured by how well it enables LLMs to accomplish real-world tasks. Use it to make implementation decisions and avoid common dead ends.
For conceptual overview (tool definitions, tool choice, tips), see shared/tool-use-concepts.md. Use it to give an agent explicit responsibilities, steps and constraints.
A developer tool for inspecting Model Context Protocol (MCP) servers. It ships as a single package, @modelcontextprotocol/inspector, that provides three ways to inspect a server:. Use it to ground design choices in named patterns, trade-offs and examples.
A curated list of awesome Model Context Protocol (MCP) servers. MCP is an open protocol that enables AI models to securely interact with local and remote resources through standardized server implementations. This list focuses on production-ready and experimental MCP servers that. Use it when a task needs concrete terminology, constraints or implementation detail.
Indexes any codebase into a knowledge graph — every dependency, call chain, cluster, and execution flow — then exposes it through smart MCP tools so AI agents never miss code. Use it to ground design choices in named patterns, trade-offs and examples.
Note that this list is continuously updating and improving. Please. Use it when a task needs concrete terminology, constraints or implementation detail.
This document provides Python-specific best practices and examples for implementing MCP servers using the MCP Python SDK. It covers server setup, tool registration patterns, input validation with Pydantic, error handling, and complete working examples. Use it to ground design choices in named patterns, trade-offs and examples.
This document provides Node/TypeScript-specific best practices and examples for implementing MCP servers using the MCP TypeScript SDK. It covers project structure, server setup, tool registration patterns, input validation with Zod, error handling, and complete working examples. Use it to ground design choices in named patterns, trade-offs and examples.
The name should be general, descriptive of the service being integrated, easy to infer from the task description, and without version numbers. Use it to ground design choices in named patterns, trade-offs and examples.
This document provides guidance on creating comprehensive evaluations for MCP servers. Evaluations test whether LLMs can effectively use your MCP server to answer realistic, complex questions using only the tools provided. Use it to navigate the topic and choose relevant methods, papers or tools.
Create MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. The quality of an MCP server is measured by how well it enables LLMs to accomplish real-world tasks. Use it to make implementation decisions and avoid common dead ends.
For conceptual overview (tool definitions, tool choice, tips), see shared/tool-use-concepts.md. Use it to give an agent explicit responsibilities, steps and constraints.
A developer tool for inspecting Model Context Protocol (MCP) servers. It ships as a single package, @modelcontextprotocol/inspector, that provides three ways to inspect a server:. Use it to ground design choices in named patterns, trade-offs and examples.
A curated list of awesome Model Context Protocol (MCP) servers. MCP is an open protocol that enables AI models to securely interact with local and remote resources through standardized server implementations. This list focuses on production-ready and experimental MCP servers that. Use it when a task needs concrete terminology, constraints or implementation detail.
Indexes any codebase into a knowledge graph — every dependency, call chain, cluster, and execution flow — then exposes it through smart MCP tools so AI agents never miss code. Use it to ground design choices in named patterns, trade-offs and examples.
Note that this list is continuously updating and improving. Please. Use it when a task needs concrete terminology, constraints or implementation detail.