{"$schema":"https://wellknown.network/schemas/agent-record-v1.json","schemaVersion":"1","id":"ag_sdwjf8vj3624","handle":"iflow-mcp-rileylemm-graphrag-mcp","url":"https://wellknown.network/agents/iflow-mcp-rileylemm-graphrag-mcp","links":{"self":"https://wellknown.network/agents/iflow-mcp-rileylemm-graphrag-mcp/record.json","html":"https://wellknown.network/agents/iflow-mcp-rileylemm-graphrag-mcp","markdown":"https://wellknown.network/agents/iflow-mcp-rileylemm-graphrag-mcp/record.md","api":"https://wellknown.network/api/v1/agents/iflow-mcp-rileylemm-graphrag-mcp","status":"https://wellknown.network/api/v1/agents/iflow-mcp-rileylemm-graphrag-mcp/status","claim":"https://wellknown.network/agents/iflow-mcp-rileylemm-graphrag-mcp/claim","claimApi":"https://wellknown.network/api/v1/claims","claimDescriptor":"https://wellknown.network/agents/iflow-mcp-rileylemm-graphrag-mcp/claim.json","badge":"https://wellknown.network/agents/iflow-mcp-rileylemm-graphrag-mcp/badge.svg","openapi":"https://wellknown.network/openapi.json","history":"https://wellknown.network/api/v1/agents/iflow-mcp-rileylemm-graphrag-mcp/history","tools":"https://wellknown.network/api/v1/agents/iflow-mcp-rileylemm-graphrag-mcp/tools"},"ard":{"identifier":"urn:air::server:iflow-mcp-rileylemm-graphrag-mcp","type":"application/mcp-server-card+json"},"kind":"mcp_server","declared":{"name":"iflow-mcp_rileylemm_graphrag-mcp","summary":"GraphRAG Model Context Protocol integration for hybrid graph and vector databases","description":"# GraphRAG MCP Server\n\nA Model Context Protocol server for querying a hybrid graph and vector database system, combining Neo4j (graph database) and Qdrant (vector database) for powerful semantic and graph-based document retrieval.\n\n## Overview\n\nGraphRAG MCP provides a seamless integration between large language models and a hybrid retrieval system that leverages the strengths of both graph databases (Neo4j) and vector databases (Qdrant). This enables:\n\n- Semantic search through document embeddings\n- Graph-based context expansion following relationships\n- Hybrid search combining vector similarity with graph relationships\n- Full integration with Claude and other LLMs through MCP\n\nThis project follows the [Model Context Protocol](https://github.com/modelcontextprotocol/python-sdk) specification, making it compatible with any MCP-enabled client.\n\n## Features\n\n- **Semantic search** using sentence embeddings and Qdrant\n- **Graph-based context expansion** using Neo4j\n- **Hybrid search** combining both approaches\n- **MCP tools and resources** for LLM integration\n- Full documentation of Neo4j schema and Qdrant collection information\n\n## Prerequisites\n\n- Python 3.12+\n- Neo4j running on localhost:7687 (default configuration)\n- Qdrant running on localhost:6333 (default configuration)\n- Document data indexed in both databases\n\n## Installation\n\n### Quick Start\n\n1. Clone this repository:\n   ```bash\n   git clone https://github.com/rileylemm/graphrag_mcp.git\n   cd graphrag_mcp\n   ```\n\n2. Install dependencies with uv:\n   ```bash\n   uv install\n   ```\n\n3. Configure your database connections in the `.env` file:\n   ```\n   # Neo4j Configuration\n   NEO4J_URI=bolt://localhost:7687\n   NEO4J_USER=neo4j\n   NEO4J_PASSWORD=password\n\n   # Qdrant Configuration\n   QDRANT_HOST=localhost\n   QDRANT_PORT=6333\n   QDRANT_COLLECTION=document_chunks\n   ```\n\n4. Run the server:\n   ```bash\n   uv run main.py\n   ```\n\n### Detailed Setup Guide\n\nFor a detailed guide on setting up the underlying hybrid database sy…","publisher":null,"homepage":null,"repository":null,"version":"0.1.0","license":null,"protocols":["mcp"],"tags":["mcp"],"pricing":null,"endpoints":[{"url":"pypi:iflow-mcp_rileylemm_graphrag-mcp","type":"package_pypi","auth":null,"probeable":false}],"skills":null,"tools":null,"extra":null,"attribution":{"kind":"pypi","name":"pypi","summary":"pypi","version":"pypi","description":"pypi"}},"derived":{"capabilities":[{"slug":"data.vector-search","name":"Vector Search","confidence":1,"provenance":"derived"},{"slug":"dev.version-control","name":"Version Control","confidence":1,"provenance":"derived"},{"slug":"knowledge.knowledge-graph","name":"Knowledge Graphs","confidence":0.756,"provenance":"derived"}],"categories":["data","dev","knowledge"],"language":"en"},"observed":{"status":"unknown","statusReason":"Distributed as a package to run locally; no network endpoint to check.","lastOkAt":null,"lastProbedAt":null,"statusComputedAt":null,"reliability30d":null,"latestObservations":[],"tools":null,"package":{"name":"iflow-mcp_rileylemm_graphrag-mcp","registry":"pypi","observedAt":"2026-09-15T20:21:13.693Z","publishedAt":"2026-02-02T03:30:10.225086Z","latestVersion":"0.1.0"},"toolSurface":null,"endpointFacts":[]},"verification":{"claimed":false,"claimedAt":null,"proofs":[]},"provenance":{"sources":[{"source":"pypi","key":"iflow-mcp_rileylemm_graphrag-mcp","url":"https://pypi.org/project/iflow-mcp_rileylemm_graphrag-mcp/","firstSeenAt":"2026-09-09T20:25:33.654Z","fetchedAt":"2026-09-15T20:19:01.285Z","normalizedAt":"2026-09-15T20:19:01.285Z"}]},"firstSeenAt":"2026-09-09T20:25:33.654Z","updatedAt":"2026-09-15T20:21:13.693Z"}