grafeo-mcp 0.1.2

MCP server exposing GrafeoDB graph database to AI agents

grafeo-mcp

MCP server that exposes GrafeoDB - an embedded graph database - to AI agents via the Model Context Protocol.

One install, zero infrastructure. The MCP server is the database.

Features

• 16 tools - graph CRUD, GQL queries, vector search, MMR, hybrid retrieval, PageRank, Dijkstra, Louvain and more
• 3 resources - graph://schema, graph://stats, graph://nodes/{id}
• 4 workflow prompts - guide agents through exploration, knowledge extraction, graph analysis and similarity search
• GQL with Cypher auto-normalization - agents trained on Cypher syntax work out of the box
• Schema-first - agents discover the graph structure before querying
• Token-aware - all tools have limit params and truncate large results
• Embedded - no separate database server to manage

Quickstart

# Install
uv tool install grafeo-mcp

# Or with pip
pip install grafeo-mcp

Claude Desktop

Add to claude_desktop_config.json:

{
  "mcpServers": {
    "grafeo": {
      "command": "grafeo-mcp",
      "env": {
        "GRAFEO_DB_PATH": "/path/to/your/graph.db"
      }
    }
  }
}

Claude Code

Add to .mcp.json in your project root:

{
  "mcpServers": {
    "grafeo": {
      "command": "grafeo-mcp",
      "env": {
        "GRAFEO_DB_PATH": "./graph.db"
      }
    }
  }
}

VS Code / Copilot

Add to .vscode/mcp.json:

{
  "servers": {
    "grafeo": {
      "command": "grafeo-mcp",
      "env": {
        "GRAFEO_DB_PATH": "${workspaceFolder}/graph.db"
      }
    }
  }
}

HTTP transport

For remote or multi-client setups:

grafeo-mcp streamable-http

Environment Variables

Variable	Description	Default
GRAFEO_DB_PATH	Path to the database file. Creates it if it doesn't exist	In-memory

Tools

Query

Tool	Description
execute_gql	Run GQL queries (Cypher syntax auto-normalized to GQL)

Graph CRUD & Traversal

Tool	Description
create_node	Create a node with labels and properties
create_edge	Create a directed edge between two nodes
get_node	Retrieve a node by ID
get_neighbors	Explore a node's neighborhood (1-hop)
search_nodes_by_label	Find nodes by label with pagination
graph_info	Schema, stats, labels, edge types, indexes

Vector Search

Tool	Description
vector_search	k-NN similarity search (HNSW)
mmr_search	Diversity-aware search (Maximal Marginal Relevance)
create_vector_index	Create HNSW index on a label + property
vector_graph_search	Hybrid: vector search + graph neighborhood expansion

Graph Algorithms

Tool	Description
pagerank	Rank nodes by importance
dijkstra	Shortest weighted path between two nodes
louvain	Community detection (Louvain modularity)
betweenness_centrality	Find bridge/bottleneck nodes
connected_components	Find disconnected subgraphs

Resources

URI	Description
graph://schema	Rich schema: labels, properties, edge types
graph://stats	Counts, memory, disk, config info
graph://nodes/{node_id}	Node details + connection summary

Prompts

Prompt	Description
explore_graph	Guided exploration of the graph structure
knowledge_extraction	Extract entities and relationships from text
graph_analysis	Structural analysis: communities, PageRank, hubs
similarity_search	Vector-powered semantic search with graph context

Development

git clone https://github.com/GrafeoDB/grafeo-mcp
cd grafeo-mcp
uv sync
uv run pytest          # Run tests
uv run ruff check .    # Lint
uv run ruff format .   # Format
uv run ty check        # Type check

See Also

• grafeo-memory includes a built-in MCP server (grafeo-memory-mcp) that wraps the high-level memory API — extract, reconcile, search, summarize. If you need AI memory management rather than raw graph access, use uv add grafeo-memory[mcp].

License

Apache-2.0