"Exploring GraphRAG and HybridRAG" matching MCP servers:

An MCP server that enables LLMs to perform semantic and fulltext searches within Neo4j while executing complex, search-augmented Cypher queries for GraphRAG applications. It provides tools for database schema discovery and supports multi-provider embeddings to facilitate advanced graph traversals.

Enables AI-powered medical information retrieval through FHIR clinical document search and GraphRAG-based exploration of medical entities and relationships. Combines vector search with knowledge graph queries for comprehensive healthcare data analysis.

Connects Claude Code to a local GraphRAG knowledge base (LightRAG + Ollama) for private, offline document retrieval. Provides tools to search, add documents, and check the RAG status, enabling Claude to reason over user's own notes, code, and papers without cloud dependencies.

Enables manufacturing traceability queries and analysis through GraphRAG, supporting semantic search, graph traversal, natural language to Cypher, defect chain retrieval, requirement traceability, and product health dashboards.

Enables AI agents to query a local knowledge graph built from document collections using hybrid search (BM25 + vector fusion) and entity-relationship extraction. Supports privacy-first, offline operation with tools for semantic search, entity graph exploration, and corpus statistics.

Deterministic code-graph (GraphRAG) over your repo for LLM agents — local-first, git-native, zero-infra, served via MCP. Python, TS/JS, Rust, Go, Java, C#.

MCP server for DocsMint, an AI-native knowledge workspace. It gives AI agents scoped access to search, read, create, update and organize documents using hybrid retrieval and GraphRAG.

Zero-trust, air-gapped Enterprise GraphRAG MCP server. Build knowledge graphs from local documents and run multi-hop, citation-grounded queries entirely offline with Ollama.

Provides GraphRAG code search to AI agents, combining structural code graph traversal with semantic and keyword retrieval so agents can find relevant code without exact name matches.

A read-only MCP server for SQL Server and PostgreSQL that enables exploring and querying databases (schemas, tables, views, procedures, indexes, foreign keys) and running arbitrary SELECT queries.

Enables access to the Hugging Face Hub API to search and retrieve information about machine learning models, datasets, and their metadata. Provides comprehensive tools for exploring the Hugging Face ecosystem including model details, dataset information, and parquet file access.

GraphRAG MCP server for querying SNOM CT medical terminology via Neo4j, offering graph_search_concept and graph_get_related tools to search and traverse concepts and relationships.

An MCP server that builds a knowledge graph from code and text documents, enabling Q\&A and implementation planning via tools like graph_create, graph_plan, and graph_query.

Enables agents to run hybrid retrieval, build token-budgeted attributed context packs, answer questions with citation and groundedness-gated release, explore temporal graph entities, evaluate claim support, and ingest screened, embedded documents through typed MCP tools.

Enhances LLM applications with deep autonomous web research capabilities, delivering higher quality information than standard search tools by exploring and validating numerous trusted sources.

Provides a comprehensive Model Context Protocol interface for RAGFlow, enabling AI models to perform semantic retrieval, manage datasets, and handle document chunks. It supports advanced features like GraphRAG and RAPTOR for sophisticated knowledge base management and natural language querying.

A lightweight, zero-configuration MCP server for source code analysis with GraphRAG capabilities, enabling structural understanding and efficient code completion from MCP-compatible AI tools.

Enables agents to answer questions about Thailand's Personal Data Protection Act by combining hybrid dense/sparse vector retrieval with knowledge-graph traversal over a 190-node graph of the statute's 96 sections. Exposes tools for hybrid section search, related-section lookup, penalty tracing from a violated section, deterministic full-text retrieval, and graph schema summaries, all running locally via Docker.

Workspace-scoped AI agent infrastructure for .NET enabling context retrieval, TODO orchestration, session logging, repository operations, GitHub automation, GraphRAG, and agent orchestration over HTTP and MCP STDIO transports.

MCP server that gives AI agents a continual learning brain with lessons, skills, memories, evidence-based refinement, rollback, and hybrid GraphRAG retrieval.