お気に入りのツールにAIを接続するためのMCPサーバーの完全なコレクションをご覧ください。
Provides an interactive AI assistant interface featuring a terminal-based CLI and an always-on-top window for real-time output monitoring.
Provides real-time financial data to Large Language Models through the Model Context Protocol.
Provides an interface to obtain setup information via a REST API using the Model Context Protocol.
Manages a simple notes system through a Model Context Protocol server, providing resources, tools, and prompts for note interaction.
Enables on-device and cloud language models to collaborate, reducing cloud costs while maintaining quality by reading long contexts locally.
Build and run polyglot Model Context Protocol (MCP) servers using WebAssembly for secure, high-performance AI agents.
Enables seamless integration with European Patent Office (EPO) OPS services via an MCP server.
Manages Azure Cloud PCs using the Microsoft Graph API.
Implements the Model Context Protocol (MCP) as a comprehensive Python backend, integrating JSON-RPC 2.0, Azure OpenAI, and Server-Sent Events for streaming responses.
Provides a Model Context Protocol (MCP) server to access and utilize the Haloscan SEO API for comprehensive SEO analysis and data retrieval.
Interacts with a Model Context Protocol server, enabling users to send commands, query data, and manage resources.
Serves markdown documents from a local directory, providing a simple interface for managing and accessing them using the Model Context Protocol (MCP).
Solves mathematical problems using a client-server architecture.
Provides a production-ready starter template for building Model Context Protocol (MCP) servers with TypeScript.
Provides AI clients the ability to remember information about users across conversations using vector search technology.
Demonstrates basic tool implementation for the Model Context Protocol (MCP) using FastMCP.
Orchestrates real-world actions like file management, Python code execution, web scraping, and shell commands via lightweight Model Context Protocol servers.
Provides PyTorch AI/ML examples for Modal Context Protocol (MCP), Agent-to-Agent (A2A), RAG, and vLLM workflows, enabling reproducible and scalable pipelines for research and deployment.
Interacts with PostgreSQL databases through the Model Context Protocol (MCP).
Facilitates interaction with MCP servers using Streamlit and Retrieval-Augmented Generation (RAG).
Automates iTerm2 terminals, allowing AI assistants to control command execution and TUI interactions through the Model Context Protocol.
Simplifies PostgreSQL database management by providing global connection pooling, PgBouncer integration, and automatic password rotation for AWS RDS.
Implements a Model Context Protocol (MCP) server for educational, experimentation, or personal use.
Provides a Model Context Protocol (MCP) server for checking domain availability using the Namecheap API.
Deploys a remote Model Context Protocol (MCP) server on Cloudflare Workers without authentication.
Demonstrates a simple application within the MCP ecosystem.
Provides Boston-area transit information by communicating with the MBTA API.
Delivers context-aware jokes from various categories for seamless integration with AI models and conversational agents via the Model Context Protocol.
Automates customer support by classifying tickets, analyzing content, generating, and sending AI-powered responses to customer email addresses.
Facilitates high-performance expert discovery, registration, and context injection using vector and graph database integration for enhanced semantic search.
Scroll for more results...