# AI Service

SiliconFlow Flux image generation server based on the MCP protocol

The Memvid MCP Server is an AI service interface based on video memory technology, supporting encoding content such as text and PDFs into video formats and providing semantic search and conversation functions.

A professional MCP server based on Google's Imagen 3.0 model that enables high-quality image generation through the Gemini API and supports seamless integration with MCP-compatible hosts such as Claude Desktop.

Intelligent AI Service Project

This project is an MCP server integrated with the Stable Diffusion image generation function, providing image generation services for AI agents. It supports generating images through the MCP protocol or direct API calls and includes development and debugging tools.

An MCP server that provides cryptocurrency news

An image generation server based on the MCP protocol, developed in Go language, and implements image generation functions through OpenAI's DALL-E API

A server that provides model context protocol services for Dify AI, supporting chat completion functions and a restaurant recommendation tool.

A cross - platform desktop application developed for the omni AI MCP Server, supporting Android and iOS development

A simple MCP server based on Perplexity AI, providing web search functionality and supporting development and deployment in the Windows environment.

A custom MCP server based on FastMCP that provides RAG functionality and multiple web search capabilities (Gemini 2.0 and Linkup), supporting local document retrieval and AI-enhanced search.

A service for directly querying OpenAI models in Claude through the MCP protocol

A high-quality image generation MCP server based on the Flux.1 Schnell model

PostgMem is a .NET - based MCP service that uses PostgreSQL and the pgvector extension to provide vector memory storage and retrieval functions for AI applications, supporting semantic search and tag filtering.

This project is an MCP server integrated with Stable Diffusion image generation functionality, providing image generation services for AI agents, including development and debugging tools and Goose platform integration capabilities.

This project is a server for the Gemini model based on the MCP protocol, encapsulating Google's Gemini API and providing functions such as text generation, function calling, and file handling.

This document categorizes and organizes various Model Context Protocol (MCP) servers to help developers integrate AI models with resources such as databases, file systems, APIs, cloud services, and automation tools.

A server plugin that implements the MCP protocol for WordPress

The AI and MCP services provided by LunarCrush support two remote connection methods, HTTP and SSE, as well as a local service launched via the command line.

An implementation of the MCP server based on Node.js and TypeScript, providing document management, context retrieval, and question - answering functions, and supporting multiple transmission methods and configuration options.

The MCP server of Claude Code, which realizes functions such as code explanation, review, and repair through local calls

An implementation of the MCP server based on the Duannao Cloud Flux model API, providing AI capabilities and supporting Claude Desktop integration.

An OCR service based on Mistral AI, supporting text and image recognition of local files and URLs

The MCP service tool collection provides API call functions for AI services on Hugging Face and Dify, supporting various NLP, CV, and speech processing tasks.

Grok-2 image generation service based on the MCP protocol

A Python-based MCP server project designed for Claude AI to test the AI's ability to write code by itself, including a complete project structure and development specifications.

An AI service project that always returns the cute Japanese reply 'きつねかわいい！！！' when users ask questions about foxes.

The MCP installer is a tool for finding, configuring, and installing MCP servers. It supports Cursor and Claude clients and needs to be used with the Smithery API.