The io.github.fabienfrfr/pbix-lineage MCP server provides a bidirectional data lineage graph for Power BI (.pbix) files, mapping relationships from source to display. It is focused on Power BI artifacts, enabling lineage traversal that supports understanding how data originates and where it appears in reports.

🛠️ Key Features

Bidirectional data lineage graph
Power BI (.pbix) file focus
Source-to-display lineage mapping

🚀 Use Cases

Trace how data flows from underlying sources to report visuals in Power BI
Follow lineage relationships in both directions for inspection and troubleshooting

⚡ Developer Benefits

Structured lineage output for Power BI projects
Helps connect source fields to displayed results within the same artifact

⚠️ Limitations

Description only specifies Power BI (.pbix) lineage; no other formats or additional capabilities are stated.

pbix-atlas

Universal lineage graph for Power BI .pbix files: source to visual field in one traversable graph.

code

source (HTTP, OData, SQL, file...) --> Power Query (M)
    --> column / calculated column --> measure (DAX)
        --> field displayed in a report visual

Install

bash

pip install pbix-atlas
# or
uv add pbix-atlas

Quick start

python

from pbix_atlas import LineageGraphBuilder, upstream, downstream, print_tree, find_nodes

graph = LineageGraphBuilder().build("my_report.pbix")

find_nodes(graph, "customer_name")
print_tree(graph, "visual_field::my_report.pbix::Page1::16::customer_name", direction="upstream")
print_tree(graph, "source::odata::example.com/odata/", direction="downstream")

Export

python

from pbix_atlas import export_graphml, export_nodes_csv, export_edges_csv, graph_summary

graph_summary(graph)                       # {'query': 70, 'column': 183, ...}
export_graphml(graph, "lineage.graphml")   # Gephi / yEd
export_nodes_csv(graph, "nodes.csv")
export_edges_csv(graph, "edges.csv")

Codegen — standalone Python pipeline

Generates a single Python file reproducing a report's full chain: source → extraction → Power Query → semantic model → Vizro dashboard. The M source is executed at runtime by the built-in interpreter, not pattern-matched. Unimplemented functions raise MRuntimeError (no silent stubs).

bash

pip install "pbix-atlas[codegen]"
pbix-atlas-codegen my_report.pbix -o pipeline.py

python

from pbix_atlas import generate_python_pipeline
generate_python_pipeline("my_report.pbix", "pipeline.py")

HTTP API / MCP server

bash

uv sync --extra api
uv run pbix-atlas    # http://127.0.0.1:8080

REST: POST /graphs, /search, /upstream, /downstream, /tree, /export, /codegen
MCP (streamable HTTP): http://127.0.0.1:8080/mcp/
Env vars: PBIX_LINEAGE_HOST, PBIX_LINEAGE_PORT

Architecture

Module	Responsibility
`models.py`	Node/edge types and shared data structures
`sources.py`	Physical source detection (configurable patterns)
`pbix_model.py`	Adapter isolating from `pbixray`
`m_lexer.py` / `m_parser.py`	Built-in M tokenizer and parser
`m_interpreter.py`	M runtime interpreter + stdlib
`dax_translate.py`	DAX → Python (supported subset)
`dax.py`	DAX reference parsing
`mquery.py`	Power Query dependencies (table-level)
`layout.py`	Internal `Report/Layout` format parsing
`graph_builder.py`	Orchestrator: builds the `networkx.DiGraph`
`navigation.py`	Upstream/downstream traversal, search, export
`codegen.py`	Python pipeline code generation
`api/app.py`	FastAPI + MCP mount (FastMCP)

Sample files

Retail Analysis Sample PBIX (Microsoft)
Sales & Returns Sample v201912 (Microsoft)

Development

bash

uv sync --extra dev
uv run pytest
uv build

🛠️ Key Features

🚀 Use Cases

⚡ Developer Benefits

⚠️ Limitations

pbix-atlas

Install

Quick start

Export

Codegen — standalone Python pipeline

HTTP API / MCP server

Architecture

Sample files

Development

Install

Configuration