The io.github.fabienfrfr/pbix-atlas MCP server provides a bidirectional data lineage graph for Power BI .pbix files, tracing relationships “from source to display.” It focuses on lineage mapping within Power BI report artifacts, enabling developers to understand how data flows through a workbook’s views and visuals.

🛠️ Key Features

Bidirectional data lineage graph
Power BI .pbix file support
Source-to-display lineage mapping

🚀 Use Cases

Analyze how upstream data feeds Power BI visuals
Trace lineage paths between data sources and displayed report elements
Use lineage graphs to study data flow within .pbix files

⚡ Developer Benefits

Provides structured lineage information for Power BI artifacts
Supports navigation in both directions via a bidirectional graph

⚠️ Limitations

Described scope is limited to Power BI .pbix lineage (“source to display”); no other formats are provided in the available data.

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