"Building a complete Arabic Scratch 3 adventure game" matching MCP servers:

A read-only MCP server that enables semantic search over a Chroma library of narrative and game design content, using multilingual embeddings (BAAI/bge-m3) and offering tools to query indexed books, retrieve page chunks, and list available titles.

Enables any MCP-compatible AI assistant to search, filter, and retrieve information from a local document collection using a hybrid search pipeline with vector, BM25, reranking, and LLM enrichment.

Provides an intelligent, graph-based memory system for LLM agents using the Zettelkasten principle, enabling automatic note construction, semantic linking, memory evolution, and autonomous graph maintenance with background optimization processes.

A Multi-Agent Conversation Protocol server for interacting with Google Play Game Services Configuration API, allowing management of game configurations through natural language commands.

Production-ready MCP server providing RAG, hierarchical memory, and 8+ tools for AI agents via the Model Context Protocol.

Enables AI agents to maintain persistent, searchable two-layer memory with 37 tools, hybrid search, knowledge graphs, and enterprise features like authentication and backups.

Enables querying PDF documents using natural language with grounded answers and source citations via a local RAG pipeline.

A Python-based MCP server that enables document-based question answering by processing PDF, TXT, and Markdown files through OpenAI's API. It provides hallucination-free responses based strictly on document content using semantic search and includes a web interface for management.

Enables document-based question answering using OpenAI's GPT-4 with semantic search and embeddings. Upload PDF, TXT, or Markdown files and get answers strictly based on document content with source attribution and confidence scores.

A local academic research assistant that indexes PDFs into a searchable vector library and exposes MCP tools for semantic search, claim extraction, contradiction detection, and multi-step research synthesis.

A local-first RAG system that indexes personal documents and enables querying them via an MCP tool, keeping data private with local embeddings.

Enables users to ingest PDF/DOCX/TXT/MD documents and ask natural language questions about them, using local embeddings and Groq-powered retrieval-augmented generation.

A Model Context Protocol server that enables AI assistants like Claude to read from, append to, and format text in Google Documents programmatically.

Provides LLM-free search tools across Confluence, JIRA, code, and project knowledge for any MCP client.

MCP server for integrating LightRAG with AI tools. Provides a unified interface for interacting with LightRAG API through the MCP protocol.

Enables AI clients to search a personal corpus, read relevant source passages, and answer questions with traceable evidence from imported sessions, notes, and articles.

A round-robin API key pool for Tavily with automatic health failover and usage tracking, enabling seamless multi-key usage with MCP clients.

A RAG-based MCP server for natural language querying of Brazilian healthcare manuals (SIH/SUS, SIA/SUS) and official ordinances. It provides 16 tools for semantic search, regulatory critique analysis, and retrieving data from SIGTAP and CNES.

Provides tools for searching Agent8 game development code examples and system prompts to assist in Agent8 SDK development.

Enables building a local, searchable knowledge base from YouTube videos, with research, transcript retrieval, and timestamped citations.

Jinni is a tool to efficiently provide Large Language Models the context of your projects. It gives a consolidated view of relevant project files complete with metadata, overcoming the limitations and inefficiencies of reading files one by one. The philosophy behind this tool is that LLM context wi

Self-hosted mem0 MCP server for Claude Code. Run a complete memory server against self-hosted Qdrant + Neo4j + Ollama while using Claude as the main LLM.

Enables users to build and manage a complete retrieval-augmented generation pipeline through conversation, including file ingestion, collection management, hybrid search, reranking, citations, and a guided setup wizard.

Enables persistent memory management with semantic vector recall, BM25 keyword search, and 3-way fused retrieval combining lexical, semantic, and tag-based signals.

A remote MCP server providing a tool for searching and querying ATprotocol documentation, helping developers easily access information while building on the protocol.

A read-only MCP server that enables AI clients to search a knowledge base and retrieve complete source documents via narrow search and fetch tools.

A TypeScript-based MCP server that enables users to manage tasks through natural conversation with Claude. Features complete CRUD operations, priority management, tagging, search functionality, and intelligent productivity insights with robust Zod validation.

A production-ready foundation for building secure, observable MCP servers with built-in authentication, rate limiting, and reference tools like database-query and semantic-search.

Generates crash-proof Python LLM pipelines from plain English descriptions. Pipelines resume from the last successful node after a crash instead of restarting from scratch. IProvider-agnostic , works with Groq, Gemini, Ollama, OpenAI, or any Python callable. Zero infrastructure, runs entirely on free-tier APIs.

A template for building Model Context Protocol servers that allow AI assistants to interact with custom data and services through queryable resources and specialized tools.