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.
Semantic Intelligence for Large-Scale Engineering. Context+ is an MCP server designed for developers who demand 99% accuracy.
This repository is a collection of reference implementations for the Model Context Protocol (MCP), as well as references to community-built servers and additional resources. The servers in this repository showcase the versatility and extensibility of MCP, demonstrating how it can be used to give Large Language Models (LLMs) secure, controlled access to tools and data sources.
• Documentation • PowerShell-Native AI Agents & Multi-Agent Orchestration PSAI is a high-agency framework designed to bridge the gap between robust systems engineering and Large Language Models. Built for developers and architects, it transforms AI from a "chatbot" into a functional engineering component within your existing PowerShell ecosystem.
agent-desktop is a native desktop automation CLI designed for AI agents, built with Rust. It gives structured access to any application through OS accessibility trees — no screenshots, no pixel matching, no browser required.
Metorial (YC F25) Open-source identity and access layer for AI agents. Connect agents to real systems with consistent auth, permissions, and observability.
🦞 OpenClaw is a great personal AI assistant — connecting all major IMs as conversation channels, supporting any LLM, running autonomously 24/7. But when we bring it into an enterprise context, new challenges naturally emerge: data scattered across individual accounts, no budget guardrails, output that stops at plain text, high-risk actions without an approval gate.
Personal AI assistant for your TradingView Desktop charts. Connects Claude Code to your locally running TradingView app via Chrome DevTools Protocol for AI-assisted chart analysis, Pine Script development, and workflow automation.
A Model Context Protocol server that supercharges tools like Claude Code, Codex CLI, and IDE clients such as Cursor or the Claude Dev VS Code extension. PAL MCP connects your favorite AI tool to multiple AI models for enhanced code analysis, problem-solving, and collaborative development.
These servers aim to demonstrate MCP features and the TypeScript and Python SDKs. Official integrations are maintained by companies building production ready MCP servers for their platforms.
A curated, opinionated list of high-quality remote Model Context Protocol (MCP) servers. This repository focuses on listing official and well-maintained MCP servers that can be accessed over the internet, helping developers discover reliable and production-ready services for their AI applications.
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.
Semantic Intelligence for Large-Scale Engineering. Context+ is an MCP server designed for developers who demand 99% accuracy.
This repository is a collection of reference implementations for the Model Context Protocol (MCP), as well as references to community-built servers and additional resources. The servers in this repository showcase the versatility and extensibility of MCP, demonstrating how it can be used to give Large Language Models (LLMs) secure, controlled access to tools and data sources.
• Documentation • PowerShell-Native AI Agents & Multi-Agent Orchestration PSAI is a high-agency framework designed to bridge the gap between robust systems engineering and Large Language Models. Built for developers and architects, it transforms AI from a "chatbot" into a functional engineering component within your existing PowerShell ecosystem.
agent-desktop is a native desktop automation CLI designed for AI agents, built with Rust. It gives structured access to any application through OS accessibility trees — no screenshots, no pixel matching, no browser required.
Metorial (YC F25) Open-source identity and access layer for AI agents. Connect agents to real systems with consistent auth, permissions, and observability.
🦞 OpenClaw is a great personal AI assistant — connecting all major IMs as conversation channels, supporting any LLM, running autonomously 24/7. But when we bring it into an enterprise context, new challenges naturally emerge: data scattered across individual accounts, no budget guardrails, output that stops at plain text, high-risk actions without an approval gate.
Personal AI assistant for your TradingView Desktop charts. Connects Claude Code to your locally running TradingView app via Chrome DevTools Protocol for AI-assisted chart analysis, Pine Script development, and workflow automation.
A Model Context Protocol server that supercharges tools like Claude Code, Codex CLI, and IDE clients such as Cursor or the Claude Dev VS Code extension. PAL MCP connects your favorite AI tool to multiple AI models for enhanced code analysis, problem-solving, and collaborative development.
These servers aim to demonstrate MCP features and the TypeScript and Python SDKs. Official integrations are maintained by companies building production ready MCP servers for their platforms.
A curated, opinionated list of high-quality remote Model Context Protocol (MCP) servers. This repository focuses on listing official and well-maintained MCP servers that can be accessed over the internet, helping developers discover reliable and production-ready services for their AI applications.