# Protocol standard

The Perses MCP Server is a local model context protocol server used to standardize the interaction between LLM hosts and the Perses application.

The MCP server project provides a standardized communication protocol for tool - agent integration, facilitating AI applications to quickly invoke various tool services.

The Model Context Protocol SDK is a standardized tool implemented in TypeScript for separating context provision from actual interaction in LLM interaction, supporting client and server development.

This project demonstrates the MCP server function of GenAIScript, supporting communication with AI models, including local models, and following the Anthropic MCP protocol standard.

The MCP project is a standardized protocol for enhancing the context understanding ability of AI models. It provides environmental information, user preferences, and conversation history in a structured manner, solving the problem of limited memory in AI systems. The project includes installation guides, environment configuration, and multiple experimental cases.

This project is a tutorial for building a Model Context Protocol (MCP) server based on Google's Gemini 2.0 model. It includes a complete code implementation, demonstrating how to enable AI models to seamlessly access external tools (such as the Brave Search API) through the MCP standard, and provides an example of a flexible architecture design.

Flex MCP Agent is a multi - functional agent project that integrates weather data acquisition, Confluence interaction, and email sending functions, standardizing the interaction between AI models and data sources using the Model Context Protocol (MCP).

A lightweight MCP server that retrieves relevant documentation content from popular AI libraries such as LangChain, LlamaIndex, and OpenAI by combining web search and content parsing, providing language models with the function of dynamically querying the latest documentation.

A collection of MCP servers provided by Props Labs for standardized interaction between AI models and external tools and services. Currently includes the MCP server for the Fireflies.ai API.

A Python-based MCP server example project that demonstrates how to create custom tools (such as sticky note management) and integrate with AI assistants (such as Claude) to achieve external data interaction and operations.

This project is a developer documentation about the Model Context Protocol (MCP) Server, which details how to build, run, and integrate the MCP Server to achieve a secure connection between AI models and real-time data, tools, and business logic.

This experiment guides users on how to deploy an MCP server and integrate it into Microsoft Copilot Studio. Through a standardized protocol, it connects AI models with data sources to achieve real - time data access and joke generation functions.

A weather data server tool developed based on the MCP SDK and TypeScript, providing real-time weather information access capabilities for large language models supporting the MCP through the Model Context Protocol (MCP).

Home Assistant MCP is a standardized protocol server that provides a bridge for AI assistants to interact with smart home systems. It supports device control, status query, and automated management, with a modular architecture and multiple transmission protocols.

Portainer MCP is an open - source project that implements the Model Context Protocol (MCP). It connects AI assistants to the Portainer container management platform through a standardized interface, supporting querying and managing Docker/Kubernetes environments.

The Cisco NSO MCP server is a Python-based implementation of the Model Context Protocol (MCP), which provides AI-driven network automation capabilities for the Cisco Network Services Orchestrator (NSO) and supports operating network devices through natural language interaction.

This project demonstrates how to integrate a Model Context Protocol (MCP) server with multiple LLM agent frameworks, including Google ADK, LangGraph, OpenAI Agents, and Pydantic - AI. It connects different LLM providers through a standardized interface and uses Logfire for tracking and visualization.