The Supabase MCP Server is a tool that connects Supabase projects with AI assistants. It standardizes the interaction between large language models (LLMs) and external services through the Model Context Protocol (MCP), enabling functions such as database management, configuration acquisition, and data querying.

BinjaLattice is a secure communication protocol designed for Binary Ninja, supporting interaction with external MCP servers and tools, and providing functions for obtaining and modifying binary analysis data.

The Browserbase MCP Server is an open - source project based on the Model Context Protocol (MCP), providing cloud browser automation capabilities, supporting web page interaction, data extraction, screenshot taking, and JavaScript execution, etc., to help LLM applications seamlessly integrate with external data sources and tools.

The JS MCP Server is a server component based on JavaScript and WASM, providing an easy - to - integrate SDK and supporting custom processors (tools) and interactions with external services (such as Postgres and HTTP).

This project implements a GitHub code search tool through the Model Context Protocol, providing a standardized external service interaction interface for AI assistants, including functions such as code search and repository viewing.

The MCP Toolbox is a comprehensive toolkit that enhances the capabilities of LLM. It enables interaction with external services and APIs through the Model Context Protocol (MCP), extending the functions of LLM.

1MCP is a unified multi - MCP server aggregation solution designed to simplify the interaction management between AI assistants and external tools.

MCP DevTools is a collection of toolkits that enables the interaction between AI assistants and external tool services through the Model Context Protocol. Currently, it provides integration support for the Jira and Linear platforms.

The MCP Server is a server that implements the Model Context Protocol, supporting standardized interaction between large language models and external tools and services, providing support for multiple tools, RESTful APIs, and automated documentation.

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 demonstrates how to integrate Model Context Protocol (MCP) tools with Microsoft Semantic Kernel to achieve seamless interaction between AI models and external data sources or tools. By standardizing the interaction between applications and AI models through the MCP protocol and combining the powerful functions of Semantic Kernel, developers can expand AI capabilities, dynamically call external functions, and simplify the orchestration of complex workflows.

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 demonstrates how to use Spring Boot AI to build an MCP client, enabling interaction with external MCP servers and supporting dynamic tool calls and data retrieval.

Gemini Rust Suite is a Rust-based Google Gemini model interaction tool suite that provides a CLI interface, tool call protocol (MCP), persistent semantic memory, and other functions, supporting integration with external services.

The MCP startup project is an AI application development framework that includes a Python server - side and a TypeScript client - side, supporting the interaction between AI models and external tools/APIs, and providing functions such as document search and conversation management.

Overview: MCP (Model Context Protocol) is a framework for AI automation through a standardized protocol, which includes key components such as hosts, clients, servers, local data sources, and remote services. The core lies in the server, which provides context, tools, and prompts for clients. MCP enables AI agents to automate tasks, such as code generation, debugging, and interaction with external services like GitHub repositories.

kuri is a Rust framework for building Model Context Protocol (MCP) servers, focusing on developer experience and code clarity. It supports defining tools and prompts through simple macros to enable interaction between LLMs and the external world.

Metis MCP Tools is a repository containing multiple toolkits, aiming to enhance the functions of Metis RAG applications. These tools provide functions such as database management, vector storage operations, document processing, and LLM interaction, and enable language models to interact with external resources through the standardized Model Context Protocol (MCP).

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.