Which integrations are available for this server?

Automatically extracts entities and relationships from unstructured text and builds organized knowledge graphs in Neo4j databases. Utilizes OpenAI's GPT models to process unstructured content and extract bibliographic data, entities, and relationships. Processes Wikipedia articles to extract structured entities and relationships for knowledge graph construction. Extracts information from YouTube video transcripts to build structured knowledge graphs. Facilitates academic research workflows by building knowledge graphs and extracting bibliographic information from papers and sources exported from Zotero.

How do I use LLM Graph Builder MCP?

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., "@LLM Graph Builder MCP Build a knowledge graph from this Wikipedia article: https://en.wikipedia.org/wiki/Neo4j" 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.

LLM Graph Builder MCP

Build knowledge graphs from any URL using Claude Desktop and Neo4j.

What is this?

This Model Context Protocol (MCP) server enables Claude to automatically extract entities and relationships from unstructured text and build knowledge graphs in Neo4j. Simply give Claude a URL (Wikipedia article, PDF, web page, YouTube video) and ask it to build a knowledge graph - it handles the rest.

Perfect for: Research, Zotero integrations, academic papers, content analysis, and building structured knowledge from unstructured sources.

What's Included

This repository is a complete, ready-to-use package containing:

llm_graph_builder_mcp/ - The MCP server code
llm-graph-builder/ - Neo4j's LLM Graph Builder backend (June 24, 2025, commit 4d7bb5e8)

Both are included so you get a tested, working version out of the box. Just clone once and you're ready to go!

Why include the backend?

Guaranteed compatibility - this MCP is tested with this exact backend version
Zero configuration headaches - everything just works together
If Neo4j updates their backend, you still have a working version

Features

Multi-source support: Wikipedia, PDFs, web pages, YouTube videos
Academic mode: Extract citations, authors, journals, and bibliographic data
Custom schemas: Define allowed entity types and relationships
Community detection: Find clusters and groups in your knowledge graph
Zero modifications: Works with unmodified llm-graph-builder backend
Local processing: Your data, your Neo4j instance, your control

Quick Start

Prerequisites

Neo4j database - Get a free instance at Neo4j AuraDB
- Create an instance and note your connection URI, username, and password
OpenAI API key - Get one here
Python 3.10+ with uv - Install uv
Claude Desktop - Download here

Step 1: Clone This Repository

# Clone the entire project (includes both MCP and backend) git clone https://github.com/henrardo/llm-graph-builder-mcp.git cd llm-graph-builder-mcp

Your directory structure will be:

llm-graph-builder-mcp/ # The MCP server llm-graph-builder/ # The backend (included)

Step 2: Set Up the Backend

# Navigate to backend cd llm-graph-builder/backend # Create environment file cp example.env .env

Edit with your credentials:

# Neo4j Connection (from your AuraDB instance) NEO4J_URI=neo4j+s://your-instance-id.databases.neo4j.io NEO4J_USERNAME=neo4j NEO4J_PASSWORD=your-auradb-password NEO4J_DATABASE=neo4j # OpenAI Configuration LLM_MODEL_CONFIG_openai_gpt_4.1=gpt-4-turbo-2024-04-09,sk-your-openai-api-key

Install and start the backend:

# Create virtual environment uv venv source .venv/bin/activate # Windows: .venv\Scripts\activate # Install dependencies uv pip install -r requirements.txt # Start the backend server uvicorn score:app --reload --port 8000

Keep this terminal running. The backend must be running for the MCP to work.

Step 3: Install the MCP

Open a new terminal (keep the backend running in the first one):

# Navigate back to the MCP directory cd llm-graph-builder-mcp # Install the MCP uvx --from . llm-graph-builder-mcp

Step 4: Configure Claude Desktop

Edit your Claude Desktop config file:

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

Add this configuration:

{ "mcpServers": { "llm-graph-builder": { "command": "uvx", "args": [ "--from", "/absolute/path/to/llm-graph-builder-mcp", "llm-graph-builder-mcp" ], "env": { "NEO4J_URI": "neo4j+s://your-instance-id.databases.neo4j.io", "NEO4J_USERNAME": "neo4j", "NEO4J_PASSWORD": "your-auradb-password", "NEO4J_DATABASE": "neo4j", "GRAPH_BUILDER_URL": "http://localhost:8000" } } } }

Important:

Replace /absolute/path/to/ with the full path to your llm-graph-builder-mcp directory
- Run pwd in the llm-graph-builder-mcp directory to get this path
- Example: /Users/yourname/projects/llm-graph-builder-mcp
Use the same credentials as in your backend .env file

Step 5: Restart Claude Desktop

Completely quit and restart Claude Desktop for the changes to take effect.

Step 6: Test It

In Claude Desktop, try:

Build a knowledge graph from this Wikipedia article: https://en.wikipedia.org/wiki/The_Hitchhiker%27s_Guide_to_the_Galaxy

Claude should now use the MCP to build a knowledge graph in your Neo4j database!

Usage Examples

Basic Usage

Build a knowledge graph from this Wikipedia article: https://en.wikipedia.org/wiki/The_Hitchhiker%27s_Guide_to_the_Galaxy

Academic Papers (with citations)

Build a knowledge graph from this PDF with bibliographic extraction: https://example.com/research-paper.pdf

Custom Schema

Build a knowledge graph from this article with these entities: - Nodes: Person, Organization, Location, Event - Relationships: Person WORKS_FOR Organization, Person ATTENDED Event https://example.com/article

With Community Detection

Build a knowledge graph from this page and enable community detection: https://en.wikipedia.org/wiki/Renaissance

Querying the Graph

This MCP builds graphs. To query them, use the separate mcp-neo4j-cypher server.

After building a graph, ask Claude:

"What entities are connected to Arthur Dent?" "Show me all the citations in my research papers" "Find communities in the knowledge graph"

Tool Reference

build_knowledge_graph_from_url

Extracts entities and relationships from a URL and builds a knowledge graph.

Parameters:

url (required): URL to process (Wikipedia, PDF, web page, YouTube)
model (optional): LLM model to use (default: openai_gpt_4.1)
allowed_nodes (optional): Comma-separated entity types (e.g., "Person,Organization,Location")
allowed_relationships (optional): Relationship triples (e.g., "Person,WORKS_FOR,Organization")
enable_communities (optional): Enable community detection (default: false)
extract_bibliographic_info (optional): Extract academic citations and references (default: false)

Supported Sources

Type	Example	Notes
Wikipedia	`https://en.wikipedia.org/wiki/...`	Any language supported
PDF URLs	`https://example.com/paper.pdf`	Full text extraction
Web pages	`https://example.com/article`	Any accessible page
YouTube	`https://www.youtube.com/watch?v=...`	Extracts from transcript

Architecture

Claude Desktop ↓ MCP Protocol llm-graph-builder-mcp (this repo) ↓ HTTP llm-graph-builder backend (FastAPI) ↓ Cypher Neo4j Database ↑ Cypher Queries mcp-neo4j-cypher (separate MCP) ↑ MCP Protocol Claude Desktop

Research & Zotero Integration

This MCP is perfect for academic research workflows:

Export PDF URLs from your Zotero library
Ask Claude to process them with bibliographic extraction
Query relationships between papers, authors, and concepts
Discover connections in your research

Example:

"Build knowledge graphs from these papers with bibliographic extraction: - https://paper1.pdf - https://paper2.pdf - https://paper3.pdf Then show me how they cite each other and what common themes they share."

Backend Version & Updates

This repository includes llm-graph-builder from June 24, 2025 (commit 4d7bb5e8). This version is tested and fully compatible with the MCP.

Using the Included Backend (Recommended)

The included backend is frozen at a known-good version. This ensures:

Everything works out of the box
No compatibility issues
Predictable behavior

Using a Newer Backend Version

If you want to use the latest llm-graph-builder:

# Remove the included backend rm -rf llm-graph-builder # Clone the latest version git clone https://github.com/neo4j-labs/llm-graph-builder.git # Follow the same setup steps in Step 2

Note: Newer versions should work (the MCP uses standard endpoints), but haven't been tested. If you encounter issues, revert to the included version.

Troubleshooting

Backend won't start

cd llm-graph-builder/backend source .venv/bin/activate uvicorn score:app --reload --port 8000

Claude doesn't see the MCP

Check config path is correct (use absolute path, not ~)
Completely quit and restart Claude Desktop (not just close the window)
Check Claude logs: ~/Library/Logs/Claude/mcp*.log (macOS)
Verify the MCP path in config matches your actual directory

"Model not found" error

Make sure your backend .env has:

LLM_MODEL_CONFIG_openai_gpt_4.1=gpt-4-turbo-2024-04-09,YOUR-API-KEY

Backend shows "Connection refused"

Ensure the backend is running on port 8000
Check GRAPH_BUILDER_URL in Claude config matches the backend URL
Backend must be running before you use the MCP

Empty graph / few entities

Enable extract_bibliographic_info for academic papers
Check OpenAI API key is valid and has credits
Verify Neo4j connection in backend .env
For PDFs: URL must be directly accessible (no authentication required)

Cache issues after updates

# Clear uvx cache uv cache clean llm-graph-builder-mcp --force # Completely quit and restart Claude Desktop

Development

# Install in development mode git clone https://github.com/henrardo/llm-graph-builder-mcp.git cd llm-graph-builder-mcp uv pip install -e .

How It Works

PDF URLs

The MCP automatically detects PDF URLs, downloads them, and uploads to the backend for full-text extraction using PyMuPDF. No binary garbage, just clean text.

Academic Extraction

When extract_bibliographic_info=true, the MCP instructs the LLM to specifically extract:

Authors, titles, journals, years, DOIs
Citations and references
Research concepts and methods
Relationships: AUTHORED, CITES, PUBLISHED_IN, DISCUSSES

Schema Specification

Define allowed entities and relationships to guide extraction:

allowed_nodes: "Person,Organization,Product" allowed_relationships: "Person,FOUNDED,Organization,Organization,PRODUCES,Product"

Zero Backend Modifications

This MCP works with the unmodified llm-graph-builder backend. It uses compatibility tricks (like sending a space character for optional parameters) to work seamlessly with the original code.

Security

Never commit:

API keys (OpenAI, etc.)
Database passwords
Real Neo4j URIs

All credentials should be in .env files or Claude Desktop config (both gitignored).

License

Apache License 2.0 - see LICENSE file for details.

This project includes the Neo4j LLM Graph Builder, which is also licensed under Apache License 2.0.

Contributing

Contributions welcome! This project aims to be a clean wrapper with zero backend modifications required.

Open an issue or pull request on GitHub.

Credits

Neo4j LLM Graph Builder by Neo4j, Inc.
FastMCP by Marvin
Model Context Protocol by Anthropic