MCP ExplorerExplorer

Model Context Protocol Templates

@XD3anon a year ago
2 MIT
FreeCommunity
AI Systems
"MCP template and guideline collection for the rapid development of Model Context Protocol (MCP) servers."

Overview

What is Model Context Protocol Templates

Model Context Protocol (MCP) is an open-source protocol released by Anthropic in November 2024, specifically designed for Large Language Models (LLMs) to standardize connections between AI and external data sources and systems. It provides a structured framework for models to integrate and utilize external context in conversations, enhancing their capabilities and improving response accuracy.

Use cases

Use cases for model-context-protocol-templates include building chatbots that leverage external databases for real-time information retrieval, developing virtual assistants that utilize external tools for task execution, and creating interactive applications that require dynamic context integration.

How to use

To use model-context-protocol-templates, developers can start by utilizing the provided SDKs for Python, TypeScript, Java, and Kotlin. The quickstart guide offers step-by-step instructions for setting up and deploying MCP servers. Additionally, debugging and inspection tools are available to help developers troubleshoot and optimize their implementations.

Key features

Key features of model-context-protocol-templates include the ability to extend LLM capabilities through resources for knowledge expansion, tools for calling external utilities, and pre-written prompts for enhanced interaction. The templates also facilitate rapid server development and integration.

Where to use

Model-context-protocol-templates can be used in various fields including AI development, natural language processing, customer service automation, and any application requiring intelligent interaction with external data sources.

Content

model-context-protocol-templates

👉 Introduction

Model Context Protocol (MCP) is an open source protocol released by Anthropic in November 2024. It is designed for large language models (LLM) and aims to solve the standardization problem of connecting AI with external data sources and systems. MCP provides a structural framework that enables models to integrate and leverage external context in conversations, thereby extending their capabilities and improving the accuracy of their responses.

MCP provides the following three capabilities to extend LLM:

  • Resources for knowledge expansion
  • Tools calls external tools
  • Prompts Pre-written prompts

Refer to the following:

👨‍💻 SDK 👩‍💻

🔘 Quickstart

🔍Debugging & Inspection

Debugging

  1. MCP Inspector

  2. Claude Desktop Developer Tools

  3. Server Logging

Inspector

npx @modelcontextprotocol/inspector <command> <arg1> <arg2>

Inspecting servers from NPM or PyPi

  • NPM package

    npx -y @modelcontextprotocol/inspector npx <package-name> <args>
    # For example
    npx -y @modelcontextprotocol/inspector npx server-postgres postgres://127.0.0.1/testdb
    
  • PyPi package

    npx @modelcontextprotocol/inspector uvx <package-name> <args>
    # For example
    npx @modelcontextprotocol/inspector uvx mcp-server-git --repository ~/code/mcp/servers.git
    

Inspecting locally developed servers

  • TypeScript

    npx @modelcontextprotocol/inspector node path/to/server/index.js args...
    
  • Python

    npx @modelcontextprotocol/inspector \
        uv \
        --directory path/to/server \
        run \
        package-name \
        args...
    

💻 MCP Servers

🧾 Templates

Python MCP Server Template

TypeScript MCP Server Template

Java MCP Server Template

Kotlin MCP Server Template

🚨 MCP Security

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