# AI integration

Titanic AI Analysis is an educational project that demonstrates how to create a server through the MCP protocol to expose the Titanic dataset to language models such as Claude for analysis. The project includes data resources and tools, supporting complex queries and statistical analysis.

A Laravel Artisan command execution server based on the MCP protocol that allows securely managing local Laravel projects through an AI assistant.

An MCP server implementation for Azure DevOps that provides a standardized protocol for AI assistants to access the Azure DevOps API

This project is an MCP server implemented for the Dev.to platform, allowing AI assistants to access and interact with Dev.to content, including functions such as getting articles, searching, creating, and updating articles.

AGE-MCP-Server is a middleware service that connects the Apache AGE graph database with the Claude AI client, supporting querying and operating graph data in PostgreSQL through natural language.

The Linear MCP Server is a server that implements the Model Context Protocol, used to connect AI assistants to the GraphQL API of the Linear project management system, providing functions such as issue tracking and project management.

Implement intelligent applications in sales scenarios through the MCP server, suitable for various AI chat environments

A lightweight server for executing PowerShell scripts and returning results, supporting AI assistant integration

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.

A minimalist MCP server example based on Java and Spring Boot, which implements weather query functions by calling the Gaode Weather API and supports AI clients to obtain weather data through the MCP protocol.

The Tavily MCP Server is an open-source tool based on the Model Context Protocol (MCP) that provides real-time web search and data extraction capabilities for AI systems such as Claude. It supports seamless integration with multiple MCP clients (such as Cline, Cursor, and Claude Desktop), enabling AI assistants to access Tavily's advanced search and content extraction functions.

CodeRAG is a tool that transforms codebases into intelligent knowledge graphs. It builds a code structure diagram through Neo4J, supports multi - language analysis, provides code quality metrics and AI integration, and helps with code review, architecture analysis, and refactoring.

Locust-MCP-Server is a server implementation based on the Model Context Protocol (MCP), used to run Locust load tests and supports seamless integration with AI development environments.

A server that implements the MCP protocol, allowing AI assistants to create and manage calendar events through the Google Calendar API, supporting OAuth2 authentication and event management functions.

An intelligent query generation and documentation assistance server based on Claude AI, providing intelligent services by analyzing API documentation.

The SDKMAN interactive command-line tool provides a convenient interface for browsing, filtering, and installing SDKs, supports all SDKMAN candidate versions, and can integrate with an AI assistant to manage the development environment through natural language.

The MCP Web Search Tool is a server tool that provides real-time web search capabilities for AI assistants. It supports pluggable search engine integration and currently mainly uses the Brave Search API.

This project demonstrates how to set up a remote MCP server on Cloudflare Workers and implement OAuth login functionality, supporting connection and tool invocation through the MCP inspector and the Claude desktop application.

This project demonstrates how to integrate MCP support of Spring AI in a Spring Boot application, including geocoding and time zone query services, and provides geographic location information through LLM interaction.

An MCP server implementation that provides Typesense search capabilities for AI models

The Maya MCP Server project implements the Model Context Protocol (MCP), allowing AI assistants to control Autodesk Maya through natural language. This project features a lightweight design, does not require installing plugins in Maya, communicates via Maya's default MEL script port, and supports dynamically adding new tool functions.

An MCP server designed for the AivisSpeech text-to-speech engine, supporting Japanese speech synthesis, multi-character voice selection, and parameter configuration, enabling seamless integration with AI assistants.

Awesome MCP FastAPI is an enhanced Model Context Protocol (MCP) service implemented based on the FastAPI framework. It provides powerful tool registration functionality and automatic documentation generation, leveraging the mature FastAPI ecosystem to achieve high-performance AI tool integration.

A tool for getting and reading Trade Companion column articles on Substack, which needs to be used with Claude AI.

The ActiveCampaign MCP server is a Model Context Protocol server for integrating with the ActiveCampaign API, providing contact management and tracking analysis functions, and supporting friendly queries from AI tools.

A server tool that enables AI assistants to directly control tasks in the Things.app via the MCP protocol, supporting task creation, update, viewing, and scheduling.

A Jira connection tool based on the Go language, supporting AI assistants to interact with Jira through the MCP protocol to achieve functions such as issue management and workflow operations.

MCP Linear is a Model Context Protocol server that implements the Linear GraphQL API, enabling AI assistants to interact with the Linear project management tool.

Lilith Shell is an enhanced MCP server that allows AI assistants to execute terminal commands in a controlled environment, with security validation and output capture functions.

The Uptime Agent MCP Server is a service that connects the Uptime Agent monitoring system to an AI assistant (such as Claude) via the Model Context Protocol (MCP), supporting functions such as real - time monitoring access, incident management, and monitoring creation.