A generative AI support plugin for Unreal Engine that integrates multiple cutting - edge LLM/GenAI model APIs, providing AI integration layer support in game development, including chat and structured output functions for models such as OpenAI, Claude, and Deepseek, and supporting the Model Control Protocol (MCP) to achieve advanced functions such as scene object control and blueprint generation.

Think MCP is a project that implements an MCP server, providing the 'think' tool to support AI agents in performing structured reasoning in complex tasks, inspired by Anthropic's research. This tool helps AI process information, backtrack, or adhere to strategies by recording the thinking process, and is suitable for scenarios such as tool output analysis, policy-intensive environments, and sequential decision-making.

The MediaCrawler MCP service upgrades the social media crawler into a standardized tool that can be directly called by AI assistants. It supports multi - platform data acquisition and has features such as externalized login, browser reuse, and structured output.

mcpx-py is a Python library for interacting with LLMs through the mcp.run tool. It supports multiple AI models and provides structured output functionality.

The Shodan MCP server is a tool that provides Shodan API and CVEDB query services, supporting functions such as network reconnaissance, DNS query, vulnerability tracking, and device discovery, and providing structured data output for platforms such as Claude Desktop.

A high-performance PDF to Markdown service based on MCP, supporting batch processing and structured output

An MCP server for command-line execution supporting structured output

DeepRe is a Deno-based AI-driven CLI tool that automatically generates in-depth research reports using Google Gemini API. It supports multi-round iterative research and automatic evaluation, and outputs structured Markdown reports.

MCPML is a Python framework for building servers compliant with the Model Context Protocol (MCP), supporting CLI tools and OpenAI agent functions, and providing features such as dynamic loading and structured output.

The mem0 MCP Server is a middleware for project management, providing structured project memory storage, semantic search, and project information management functions, and supporting interaction with the MCP host application through the standard input - output protocol.

This project uses natural language processing technology to automatically extract key elements from guarantee texts, split clauses, and highlight key points, and output structured data, providing an automated processing solution for legal and financial professionals.

An RSS MCP server based on TypeScript that supports parsing standard RSS/Atom feeds and RSSHub feeds, providing multi - instance polling and structured content output.

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.

The Firecrawl MCP Server is a web crawler service based on the Firecrawl API, providing functions such as web scraping, content search, site crawling, and structured data extraction, and supporting multiple configuration options and output formats.

An automatic merge request summary generation tool based on the MCP protocol. It intelligently categorizes changes by analyzing Git commit history and generates structured summaries, supporting Markdown and JSON output formats.

The JobSpy MCP server is a tool that provides cross - platform job search functionality for AI assistants through the Model Context Protocol (MCP), supporting job searches on multiple platforms and structured data output.

The Lightning Network MCP Server is a service that allows large language model agents to query Lightning Network node data through natural language. It supports gRPC and LNC connections and provides readable summaries and structured JSON output.

The project includes both MCP and REST APIs. The MCP interface serves clients such as Claude, and the REST interface is used for reports and automation tasks. Current functions include mailbox search and OCR processing. The next step is to implement PDF attachment processing and structured output.

An MCP server based on TypeScript for executing commands and returning structured output.

An MCP server based on TypeScript for executing commands and returning structured output, supporting integration with Claude Desktop.

PydanticAI is a Python agent framework developed by the Pydantic team, aiming to simplify the development of production-grade applications based on generative AI. It supports multiple AI models, integrates Pydantic validation and structured output, provides a dependency injection system, streaming responses, and graph support, and seamlessly integrates with Pydantic Logfire. It is suitable for building AI applications that require type safety and efficiency.