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LadybugDB MCP Server
An MCP server implementation that interacts with LadybugDB graph databases, providing Cypher query capabilities to AI Assistants and IDEs.
About LadybugDB
LadybugDB is an embedded graph database built for query speed and scalability. It is optimized for handling complex join-heavy analytical workloads on very large graphs.
Key features:
- Property Graph data model with Cypher query language
- Embedded database - runs in-process with your application
- Columnar disk-based storage for analytical performance
- Strongly typed schema with explicit data types
- JSON support through the json extension
- Interoperability with Parquet, Arrow, DuckDB, and more
Components
Prompts
The server provides one prompt:
ladybugdb-initial-prompt: A prompt to initialize a connection to LadybugDB and start working with it
Tools
The server offers one tool:
query: Execute a Cypher query on the LadybugDB database- Inputs:
query(string, required): The Cypher query to execute
- Inputs:
All interactions with LadybugDB are done through writing Cypher queries.
Result Limiting: Query results are automatically limited to prevent using up too much context:
- Maximum 1024 rows by default (configurable with
--max-rows) - Maximum 50,000 characters by default (configurable with
--max-chars) - Truncated responses include a note about truncation
Installation
Using pip (recommended)
pip install mcp-server-ladybug
mcp-server-ladybug --db-path :memory:
Note: Replace
:memory:with a path like/path/to/local.lbdbto persist data to disk.
Using Docker
docker run -it --rm ghcr.io/ladybugdb/mcp-server-ladybug:latest --db-path :memory:
Note: Replace
:memory:with a path like/path/to/local.lbdbto persist data to disk.
Using uvx
uvx mcp-server-ladybug --db-path :memory:
Note: Replace
:memory:with a path like/path/to/local.lbdbto persist data to disk.
From source
git clone https://github.com/LadybugDB/mcp-server-ladybug.git
cd mcp-server-ladybug
uv pip install -e .
mcp-server-ladybug --db-path :memory:
Note: Replace
:memory:with a path like/path/to/local.lbdbto persist data to disk.
Command Line Parameters
| Parameter | Type | Default | Description |
|---|---|---|---|
--transport | Choice | stdio | Transport type. Options: stdio, sse, stream |
--port | Integer | 8000 | Port to listen on for sse and stream transport mode |
--host | String | 127.0.0.1 | Host to bind the MCP server for sse and stream transport mode |
--db-path | String | :memory: | Path to LadybugDB database file |
--max-rows | Integer | 1024 | Maximum number of rows to return from queries |
--max-chars | Integer | 50000 | Maximum number of characters in query results |
Usage with Claude Desktop
Add the following to your claude_desktop_config.json:
{
"mcpServers": {
"mcp-server-ladybug": {
"command": "uvx",
"args": [
"mcp-server-ladybug",
"--db-path",
":memory:"
]
}
}
}
Note: Replace
:memory:with a path like/path/to/local.lbdbto persist data to disk.
Cypher Query Examples
Create a graph schema
CREATE NODE TABLE Person (id INT64 PRIMARY KEY, name STRING, age INT64);
CREATE NODE TABLE City (name STRING PRIMARY KEY, population INT64);
CREATE REL TABLE Follows (FROM Person TO Person, since INT64);
CREATE REL TABLE LivesIn (FROM Person TO City);
Import data from CSV
COPY Person FROM 'persons.csv';
COPY City FROM 'cities.csv';
COPY Follows FROM 'follows.csv';
Query relationships
MATCH (a:Person)-[:Follows]->(b:Person)
WHERE a.age > 25
RETURN a.name, b.name, a.age;
Use JSON data (requires json extension)
INSTALL json;
LOAD json;
CREATE NODE TABLE Product (id INT64 PRIMARY KEY, details JSON);
COPY Product FROM 'products.json';
MATCH (p:Product)
WHERE json_extract(p.details, '$.category') = 'electronics'
RETURN p.id, json_extract(p.details, '$.name') AS product_name;
Development
uv pip install -e .
python -m mcp_server_ladybug --db-path :memory:
Note: Replace
:memory:with a path like/path/to/local.lbdbto persist data to disk.
License
MIT License
