Which integrations are available for this server?

Supports Amazon Bedrock embedding models for performing search-augmented Cypher queries and vector searches. Extends Neo4j databases with vector search, fulltext search, and the ability to execute read-only Cypher queries for GraphRAG applications. Enables local semantic search capabilities by utilizing Ollama for embedding generation. Integrates OpenAI embedding models to enable semantic similarity searches within Neo4j vector indexes.

How do I use Neo4j GraphRAG MCP Server?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Neo4j GraphRAG MCP Server Find movies about space travel and list their directors and genres" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

Neo4j GraphRAG MCP Server

PyPI version Python 3.10+ License: MIT

An MCP server that extends Neo4j with vector search, fulltext search, and search-augmented Cypher queries for GraphRAG applications.

Inspired by the Neo4j Labs server. This server adds vector search, fulltext search, and the innovative search_cypher_query tool for combining search with graph traversal.

Overview

This server enables LLMs to:

🔍 Search Neo4j vector indexes using semantic similarity
📝 Search fulltext indexes with Lucene syntax
⚡ Combine search with Cypher queries via search_cypher_query
🕸️ Execute read-only Cypher queries

Built on LiteLLM for multi-provider embedding support (OpenAI, Azure, Bedrock, Cohere, etc.).

Related: For the official Neo4j MCP Server, see neo4j/mcp. For Neo4j Labs MCP Servers (Cypher, Memory, Data Modeling), see neo4j-contrib/mcp-neo4j.

Installation

# Using pip pip install mcp-neo4j-graphrag # Using uv (recommended) uv pip install mcp-neo4j-graphrag

Configuration

Claude Desktop

Edit the configuration file:

macOS/Linux: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%\Claude\claude_desktop_config.json

{ "mcpServers": { "neo4j-graphrag": { "command": "uvx", "args": ["mcp-neo4j-graphrag"], "env": { "NEO4J_URI": "neo4j+s://demo.neo4jlabs.com", "NEO4J_USERNAME": "recommendations", "NEO4J_PASSWORD": "recommendations", "NEO4J_DATABASE": "recommendations", "OPENAI_API_KEY": "sk-...", "EMBEDDING_MODEL": "text-embedding-ada-002" } } } }

Note: uvx automatically downloads and runs the package from PyPI. No local installation needed!

Cursor

Edit ~/.cursor/mcp.json or .cursor/mcp.json in your project. Use the same configuration as above.

Reload Configuration

Claude Desktop: Quit and restart the application
Cursor: Reload the window (Cmd/Ctrl + Shift + P → "Reload Window")

Tools

`get_neo4j_schema_and_indexes`

Discover the graph schema, vector indexes, and fulltext indexes.

💡 The agent should automatically call this tool first before using other tools to understand the schema and indexes of the database.

Example prompt:

"What is inside the database?"

`vector_search`

Semantic similarity search using embeddings.

Parameters: text_query, vector_index, top_k, return_properties

Example prompt:

"What movies are about artificial intelligence?"

`fulltext_search`

Keyword search with Lucene syntax (AND, OR, wildcards, fuzzy).

Parameters: text_query, fulltext_index, top_k, return_properties

Example prompt:

"find people named Tom"

`read_neo4j_cypher`

Execute read-only Cypher queries.

Parameters: query, params

Example prompt:

"Show me all genres and how many movies are in each"

`search_cypher_query`

Combine vector/fulltext search with Cypher queries. Use $vector_embedding and $fulltext_text placeholders.

Parameters: cypher_query, vector_query, fulltext_query, params

Example prompt:

"In one query, what are the directors and genres of the movies about 'time travel adventure' "

Environment Variables

Variable	Required	Default	Description
`NEO4J_URI`	Yes	`bolt://localhost:7687`	Neo4j connection URI
`NEO4J_USERNAME`	Yes	`neo4j`	Neo4j username
`NEO4J_PASSWORD`	Yes	`password`	Neo4j password
`NEO4J_DATABASE`	No	`neo4j`	Database name
`EMBEDDING_MODEL`	No	`text-embedding-3-small`	Embedding model (see below)

Embedding Providers

Set EMBEDDING_MODEL and the corresponding API key:

Provider	Model Format	API Key Variable
OpenAI	`text-embedding-ada-002`	`OPENAI_API_KEY`
Azure	`azure/deployment-name`	`AZURE_API_KEY`, `AZURE_API_BASE`
Bedrock	`bedrock/amazon.titan-embed-text-v1`	`AWS_ACCESS_KEY_ID`, `AWS_SECRET_ACCESS_KEY`
Cohere	`cohere/embed-english-v3.0`	`COHERE_API_KEY`
Ollama	`ollama/nomic-embed-text`	(none - local)