Haiku RAG is an intelligent retrieval - augmented generation system built on LanceDB, Pydantic AI, and Docling. It supports hybrid search, re - ranking, Q&A agents, multi - agent research processes, and provides local - first document processing and MCP server integration.

This project builds a bridge between Claude Code and Google Gemini AI, enabling direct calls to Gemini in the Claude Code environment for Q&A, code review, and creative brainstorming, providing a convenient AI collaboration tool.

A RAG system based on Python code analysis. It parses the code structure through AST and stores it in the Weaviate vector database, providing code query, natural language Q&A, and visualization functions, and supporting multi-codebase management and dependency analysis.

Memex is a tool based on the Model Context Protocol (MCP) for analyzing web page content and adding it to a knowledge base, supporting Q&A interactions via the Claude API.

A document Q&A MCP server based on Langflow that provides a document query interface implemented in TypeScript and supports integration with Claude Desktop.

The Biel.ai MCP server connects the IDE to product documentation and enables AI tools to access and search the company's knowledge base through RAG technology, providing intelligent code completion and technical Q&A.

The MCP server of TiDB AI, providing a Q&A interface for Cursor

This project provides a guide for integrating AI chat functionality into Android Studio, enabling developers to interact with code and projects using natural language, including functions such as code Q&A, modification, explanation, and debugging.

An HTTP SSE client/server project based on Docker containers, supporting tool query and Q&A functions

A UK parliamentary data service project based on FastMCP, providing query and semantic search functions for data such as parliamentary members, constituencies, election results, government positions, parliamentary Q&A, and debate records, supporting access through MCP clients such as Claude.

A Vercel AI SDK documentation query proxy service based on the MCP protocol, providing AI-driven document search and Q&A functions, and supporting integration with MCP clients such as Claude Desktop.

An MCP service that prevents AI hallucinations. When AI is uncertain, it can ask humans for help instead of being over - confident, improving development efficiency through a simple Q&A mechanism.

An MCP server that enables user interaction via Telegram, providing functions for Q&A, notifications, file transfer, and project packaging.

This project builds a RAG-based HR chatbot. Through the MCP server as the function call center, it realizes the functions of PDF document uploading, parsing, retrieval, and natural language Q&A.

This project is an MCP server based on Google Cloud Vertex AI Gemini model, providing a rich toolset for code assistance and general Q&A, supporting web search-enhanced answers, document explanation generation, file system operations, and other functions.

brain - trust is an MCP server that connects the IDE with OpenAI through 3 simple tools, providing intelligent Q&A and structured plan review functions, supporting Docker deployment and integration with multiple IDEs

An MCP service for YouTube video analysis based on the Google Gemini Vision API, providing functions such as video description, summarization, Q&A, and key segment extraction.

A Godot documentation query assistant based on Retrieval Augmented Generation (RAG), achieving intelligent Q&A through vectorization technology and semantic search.

Document Q&A MCP server based on Langflow

A tool for interacting with globally published books through the MCP client, supporting long - text pagination and caching, and planning to integrate Gemini Q&A and Sci - Hub resources.

Flexible GraphRAG is a flexible platform that supports multi - data source document processing, automatic knowledge graph construction, hybrid search (full - text, vector, graph), and AI Q&A. It includes a FastAPI backend, an MCP server, and various front - end interfaces.

A server based on the Model Context Protocol (MCP) that provides Retrieval Augmented Generation (RAG) capabilities, integrates clients such as Cursor IDE and Claude Desktop, and enables domain knowledge Q&A, intelligent retrieval, and context-aware response generation.

This is an MCP document Q&A server based on the GroundX API that supports uploading documents and conducting intelligent Q&A on the document content.

A Discord bot server integrated with the Mastra MCP component, providing intelligent Q&A, thread management, and command processing functions.

The MCP Docling Server is a server that provides document processing capabilities, supporting various document conversions and extraction functions, including format conversion, table extraction, and Q&A generation. It can be integrated with the Llama Stack to enhance the capabilities of LLM applications.

BCRP-MCP is a server based on the Model Context Protocol (MCP) that provides a standardized access interface to the economic and financial time-series data of the Central Reserve Bank of Peru (BCRP). It supports exploring Peru's economic indicators through keyword search, data analysis, and Q&A functions.

A server-side project based on the Model Context Protocol (MCP) that provides retrieval-augmented generation (RAG) capabilities. It can serve as a bridge between clients such as Cursor IDE and Claude Desktop and Contextual AI agents, enabling intelligent Q&A based on the knowledge base and context-aware responses.

A document query MCP server based on the Gemini API, providing intelligent Q&A services for technical documents.

A Q&A chatbot project about the EU AI Act, using a hybrid retrieval method (vector search + knowledge graph), providing local running and AWS Lambda deployment options.

A lightweight MCP server project for loading local PDF files, extracting and chunking content, building semantic search indexes, and sending relevant paragraphs to Claude Desktop for document-based Q&A.

An MCP server based on TypeScript that can crawl website content and store it in the Qdrant vector database, providing a Q&A tool for users to query the indexed content.

List of Tablestore MCP server implementations, including Java and Python getting - started examples and a knowledge base Q&A system based on the MCP architecture, providing AI technical support.

A Pokémon Q&A assistant based on RAG technology, integrating the PokeAPI database and AI capabilities to provide accurate information interaction

This project provides a single-player Jeopardy! game experience based on J-Archive for VSCode users, enabling Q&A interaction through GitHub Copilot.

The Auggie MCP server integrates the Auggie CLI tool into the MCP ecosystem, providing code repository Q&A and change implementation functions, and supporting clients such as Cursor and Claude.

An MCP server template based on the Bun runtime, specifically designed for building AI Q&A tools, providing interactive quiz functions with multiple categories and difficulty levels.

An MCP server implemented for the Perplexity API, supporting both Q&A and search modes, and can be simply configured through environment variables.

A TypeScript-based MCP server that integrates the Kagi Search API, provides functions such as web search, and plans to expand tools such as summary generation and quick Q&A.

A Python-based chat workflow engine that integrates FastAPI, CLI, and Google ADK for unified AI Q&A processes and extended functionality

An AI-based food nutritional analysis assistant that identifies foods through images, calculates calorie and protein content, and supports nutritional knowledge Q&A and conversation memory functions.