DBT Manifest MCP Server

A FastMCP server for analyzing DBT manifests with automatic schema version detection and lineage tracking.

Author: David B Company: DABBLEFISH LLC License: MIT

Features

Automatic Schema Version Detection: Supports DBT manifest schema versions v0-v12
Version-Adaptive Parsing: Backward compatibility with legacy manifest formats
SQLite Database Storage: Efficient querying and data persistence
Lineage Analysis: Upstream and downstream dependency tracking
Model Information: Detailed model metadata and compiled code access
PEP-8 Compliant: Professional Python package structure

Installation

From PyPI (when published)

pip install dbt-manifest-mcp

From Source

git clone https://github.com/dabblefish/dbt-manifest-mcp.git
cd dbt-manifest-mcp
pip install -e .

Development Installation

git clone https://github.com/dabblefish/dbt-manifest-mcp.git
cd dbt-manifest-mcp
pip install -e ".[dev]"

Usage

Running the Server

# Using the installed command
dbt-manifest-mcp

# Or using Python module
python -m dbt_manifest_mcp.server

Environment Variables

DBT_MANIFEST_PATH: Path to the DBT manifest.json file (required)
DBT_DB_PATH: Path to SQLite database file (optional, defaults to ./dbt_manifest.db)

Example

export DBT_MANIFEST_PATH="/path/to/your/manifest.json"
export DBT_DB_PATH="./dbt_manifest.db"
dbt-manifest-mcp

Available Tools

1. refresh_manifest

Refresh DBT manifest data by parsing and storing in SQLite database.

Parameters:

manifest_path (optional): Path to the DBT manifest.json file

Returns: Success message with statistics

2. get_upstream_lineage

Get upstream lineage for a DBT model.

Parameters:

model_id: Unique ID of the DBT model (e.g., 'model.my_project.my_model')

Returns: Dictionary with model_id, upstream_models list, and count

3. get_downstream_lineage

Get downstream lineage for a DBT model.

Parameters:

model_id: Unique ID of the DBT model (e.g., 'model.my_project.my_model')

Returns: Dictionary with model_id, downstream_models list, and count

4. get_model_info

Get detailed information about a DBT model including parent/child counts and compiled code.

Parameters:

model_id: Unique ID of the DBT model (e.g., 'model.my_project.my_model')

Returns: Dictionary with detailed model information

5. get_schema_info

Get information about the loaded DBT manifest schema version, supported features, and database statistics.

Returns: Dictionary with version info, features, and statistics

Schema Version Support

The server automatically detects and adapts to different DBT manifest schema versions:

v0-v3: Legacy format with basic node structure
v4+: Modern format with parent_map and child_map
v12: Latest format with enhanced metadata

Version-Specific Features

Version	Parent Map	Child Map	Node Structure	Metadata Location
v0-v3	❌ (built from dependencies)	❌ (built from dependencies)	Legacy	Root
v4-v11	✅	✅	Modern	Metadata
v12	✅	✅	Modern	Metadata

Database Schema

The server creates the following SQLite tables:

metadata: Schema version and manifest metadata
nodes: DBT models, tests, and other nodes
sources: DBT source definitions
macros: DBT macro definitions
parent_map: Parent-child relationships
child_map: Child-parent relationships

Example Usage

# After starting the server, you can use the tools via MCP client

# Refresh manifest data
refresh_manifest("/path/to/manifest.json")

# Get upstream dependencies
upstream = get_upstream_lineage("model.my_project.customer_orders")

# Get downstream dependencies
downstream = get_downstream_lineage("model.my_project.raw_customers")

# Get detailed model information
model_info = get_model_info("model.my_project.customer_summary")

# Get schema version information
schema_info = get_schema_info()

Error Handling

The server includes comprehensive error handling for:

Missing or invalid manifest files
Unsupported schema versions
Database connection issues
Invalid model IDs

License

MIT License - see LICENSE file for details.

This server cannot be installed

security - not tested

license - permissive license

quality - not tested

How are these scores calculated?

hybrid server

The server is able to function both locally and remotely, depending on the configuration or use case.

Enables the analysis of DBT manifests with automatic schema version detection and lineage tracking, allowing users to query model dependencies, access compiled code, and get detailed model information.

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