mcp-graph-engine
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@mcp-graph-engineMap out the dependencies in this codebase"
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.
MCP Graph Engine
A graph database for AI assistants via the Model Context Protocol. Build relationship graphs, run analysis algorithms, and visualize in real-time.
Installation
Requirements: Python 3.10+, MCP-compatible client (Claude Code, Claude Desktop, Cursor)
pipx install mcp-graph-engineAdd to your MCP config:
{
"mcpServers": {
"graph-engine": {
"command": "mcp-graph-engine"
}
}
}Client | Config Location |
Claude Code |
|
Claude Desktop |
|
Cursor |
|
Restart your client after adding the config.
Related MCP server: CodeRAG
What You Can Do
Just ask your AI assistant:
"Map out the dependencies in this codebase"
"Build a graph of the characters in this document"
"What's the most critical component?"
"Are there any circular dependencies?"
"Show me the path from X to Y"
"Visualize the graph"
The AI handles the tool calls. You get a live visualization at http://localhost:8765.
Features
Analysis - PageRank, cycle detection, shortest paths, connected components
Visualization - Live D3 force-directed graph in your browser
Import/Export - DOT, CSV, GraphML, JSON, Mermaid
Configuration
Variable | Default | Description |
|
| Visualization server port |
|
| Visualization server host |
|
| Enable/disable visualization |
Notes
Transient - Graphs live in memory. Export to JSON for persistence.
Fuzzy matching -
pipx install mcp-graph-engine[embeddings]for semantic node matching.
License
MIT
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Maintenance
Resources
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If you are the server author, to access and configure the admin panel.
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