MCP Go is a server-side framework for the Model Context Protocol (MCP) implemented in Go, used to connect LLM applications with external data sources and tools. It provides core functions such as resource management, tool invocation, and prompt templates, and supports session management and middleware extension.

Context7 MCP is a service that provides real-time, version-specific documentation and code examples for AI programming assistants. It is directly integrated into prompts through the Model Context Protocol to solve the problem of LLMs using outdated information.

Security Detections MCP is a server based on the Model Context Protocol that allows LLMs to query a unified security detection rule database covering Sigma, Splunk ESCU, Elastic, and KQL formats. The latest version 3.0 is upgraded to an autonomous detection engineering platform that can automatically extract TTPs from threat intelligence, analyze coverage gaps, generate SIEM-native format detection rules, run tests, and verify. The project includes over 71 tools, 11 pre-built workflow prompts, and a knowledge graph system, supporting multiple SIEM platforms.

The Dify MCP client plugin, as an agent strategy plugin, allows ReAct agent nodes to connect to the MCP server, converting the list of tools, resources, and prompts into Dify tools for LLM to call.

MCP is an open-source protocol for standardizing LLM interactions, providing a unified framework for connecting data sources, obtaining context, using tools, and executing standard prompts. Project examples demonstrate how to build MCP servers and clients to implement knowledge base chatbot functionality.

WebMCP is a project that allows websites to support client-side LLMs (Large Language Models). It enables the sharing of tools, resources, and prompts between websites and LLMs through a local WebSocket server without the need for API keys and supports multiple models.

Deep-Co is a cross-platform chat client developed based on Compose Multiplatform. It supports multiple LLM API providers, including OpenRouter, Anthropic, Grok, OpenAI, etc., and is compatible with OpenAI API and local models. It has functions such as chat record management, prompt management, role adaptation, TTS, etc., and supports multiple languages and theme customization.

A PHP-based tool for quickly building MCP servers, supporting the creation of tools, exposure of resources, and definition of prompts, simplifying LLM context management.

MeiGen AI Design MCP is an open-source MCP server that provides creative design capabilities for LLM, supports local ComfyUI and cloud API, has a built-in library of over 1300 professional prompts, and can achieve multi-directional parallel image generation.

Promptheus is an intelligent prompt optimization tool that helps users optimize LLM prompts and improve the effect of AI conversations through adaptive questioning and multi - model support.

MCP Advisor is a server that provides access to the complete Model Context Protocol specification. It helps LLMs and humans understand the MCP specification through prompts and resources, including JSON schemas and detailed documentation.

The MCP Chain of Draft Prompt Tool is a tool that enhances the reasoning ability of LLMs. By converting standard prompts into Chain of Draft or Chain of Thought format, it significantly improves reasoning quality and reduces token usage.

The Model Context Protocol (MCP) is an open - source protocol released by Anthropic, aiming to solve the standardization problem of connecting large language models (LLMs) with external data sources and systems. It provides a structured framework that enables models to integrate and utilize external context, thereby expanding their capabilities and improving response accuracy. MCP supports functions such as knowledge expansion, external tool calls, and pre - written prompts.

The MCP Python SDK is a Python library that implements the Model Context Protocol (MCP), used to standardize the interaction between LLM applications and servers, supporting the exposure and management of resources, tools, and prompts.

The Golang MCP Server SDK is a server-side development toolkit for building servers that comply with the Model Context Protocol (MCP) specification, supporting interaction with LLMs through standard transport methods (such as stdio and Server-Sent Events), and providing core functions such as resources, tools, and prompts.

The OracleDB MCP Server is a server that provides Oracle database table/column context to LLMs, supporting interaction with the database through LLM prompts, generating SQL statements, and returning results.

This project is an OpenAPI MCP server used to provide pre - configured REST API context for LLMs, enabling LLMs to interact with REST APIs through prompts.

The MCP Python SDK is a Python library that implements the Model Context Protocol (MCP) for standardizing the interaction between LLM applications and servers, supporting the exposure and management of resources, tools, and prompts.

An enterprise-level high-performance MCP server supporting Wasm and Rust, providing an integration platform for tools, resources, and process prompts for LLMs.

A general-purpose Model Context Protocol (MCP) server that allows any LLM or AI agent to access expert skills in the local file system. Enables cross-platform skill sharing through structured prompts and supports lazy loading to save context tokens.

The Roblox Studio MCP server is a TypeScript implementation designed specifically for Roblox Studio development, providing resources, tools, and prompts, and supporting LLM applications to access Roblox Studio documentation, templates, and code generation features through standardized interfaces.

The Model Context Protocol (MCP) is a standardized protocol introduced by Anthropic for the interaction between large language models (LLMs) and external tools and functions, as well as for injecting context into LLMs. It enables models to request real-time information, perform operations in external systems, access professional knowledge bases, and interact with APIs and services. The MCP is similar to a USB hub, serving as a standard interface that reduces the risk of API changes because each MCP server is maintained by its own company, and users only need to integrate it. The MCP server provides three functions: tools, resources, and prompts.

This project is a Go implementation of the Model Context Protocol (MCP), a JSON-RPC based protocol for client-server interaction in LLM applications. The project includes Go structure definitions, client implementation, and example code, supporting interaction with resources, tools, prompts, etc.

A demonstration of a RAG application integrated with an MCP server, featuring document retrieval, context-aware prompt generation, and LLM API integration.

A TypeScript-based development template for Model Context Protocol (MCP) applications, providing standardized components such as resources, tools, and prompts, supporting multiple transport protocols, and used to build secure services for interacting with LLMs.

An MCP server that converts documentation into LLM-friendly prompts

VectorMCP is a Ruby library for implementing the server-side functions of the Model Context Protocol (MCP), enabling large language models (LLMs) to discover and interact with external tools, resources, and prompt templates.

The project uses TypeScript and Node.js to develop an MCP server and integrates the LLM Studio API to optimize question answering, supporting dynamic prompt optimization

This project includes the MCP server and VSCode extension, which generates HTML, CSS, and JavaScript applications through natural language prompts and uses Google LLM technology to achieve AI code generation.

Prompt Cleaner is an MCP server based on TypeScript, providing prompt cleaning tools and health check functions. The main functions include sensitive information desensitization, structured output, client-friendly content standardization, support for configuring LLM parameters through environment variables, and following the single model strategy to ensure behavior determinacy.

This project installs an MCP (Model Context Protocol) server to provide the configured Oracle database tables/columns as context to the LLM (Large Language Model), enabling the LLM to interact with the Oracle database, generate SQL statements, and return results through LLM prompts.

MCP is a communication protocol that supports multiple LLM clients and provides tool invocation and prompt template functions.

An MCP server implementation based on Node.js and TypeScript, providing a standardized LLM context interaction protocol, including functions such as calculation tools, system information, message reflection, and prompt templates.

This project implements a Model Context Protocol (MCP) server, which provides a standardized data source access method for LLMs and supports three core capabilities: resources, tools, and prompts.

The Senechal MCP Server is a middleware that provides health data interfaces for LLM applications. It connects the Senechal API and language models through a standardized protocol, supporting resource loading, tool calls, and prompt template functions.

The MCP protocol standardizes the interaction between applications and LLMs, and realizes unified management of tool invocation, resource access, and prompt templates through a modular architecture (server, client, host). This project demonstrates how to build an MCP server and client that support knowledge base queries, context selection, and analysis processes.

This is a prompt optimization server based on the Model Context Protocol (MCP) that uses AI to enhance and clean the original prompts, making them clearer, more actionable, and more effective. It supports local and cloud LLMs and provides quality assessment and retry strategies.

An MCP server tool for testing the effects of prompts from different LLM providers, supporting multi - turn conversations and comparative testing of OpenAI and Anthropic models.

The Xircuits MCP Component Library is a visual programming tool library for implementing Model Context Protocol (MCP) servers. It provides components for creating, configuring, and running MCP servers, and supports interactions with LLM applications through resources, tools, and prompt templates.

mcp-server-llmling is an MCP protocol server based on YAML configuration, providing functions such as resource management, tool systems, and prompt templates for LLM applications.