Pbi Context
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AI context engine for Power BI models — turns .pbit/.pbip (TMDL) into indexed, queryable context for LLM agents (MCP server, CLI, docs). Zero dependencies.
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เปิดpbi-context is the zero-dependency context compiler that lets any AI agent read, query, and audit a Power BI model — via CLI, indexed JSON, or a read-only MCP server.
Who it's for: data engineers documenting dashboards, consultants auditing models they didn't build, and anyone connecting an AI agent (Claude, GPT, Copilot) to a Power BI model's structure.
pip install pbi-context
# From a .pbit file...
pbi-context --input "data/pbit/my-model.pbit"
# ...or a PBIP project (folder, .pbip marker, or .SemanticModel/ — auto-detected)
pbi-context --input "data/pbip/my-model/"
cat "output/my-model.pbit/model_documentation.md"
Result: 7 files in output/<model-name>/ in seconds — human-readable Markdown, JSON/JSONL
context for AI agents, and an indexed, queryable version for large models.
The command generates a folder in output/ with all documentation files:
output/my-model.pbit/ (or output/my-model/ for PBIP)
├── metadata.json # Structured model metadata
├── model_documentation.md # Human-readable documentation
├── agent_context.json # LLM-optimized context (top-20 measures)
├── model_context.jsonl # JSONL format for embeddings/RAG
├── index.json # Lightweight index + pointers
├── relationships.json # All relationships
└── tables/
├── Sales.json # Full detail per table
└── ...
cat "output/my-model.pbit/model_documentation.md" | head -n 15
# my-model - Power BI Data Model
**Generated:** 2025-12-22 14:23:29
## Model Summary
- **Business Tables:** 9
- **Total Columns:** 23
- **Total Measures:** 44
- **Relationships:** 9
Use the JSON context directly in Python (or point an AI agent at it via --query or
--mcp-serve — see Use Cases below):
import json
with open("output/my-model.pbit/agent_context.json", "r", encoding="utf-8") as f:
context = json.load(f)
print(f"Model: {context['model_name']}")
print(f"Key measures: {len(context['key_measures'])}")
print(f"First measure: {context['key_measures'][0]['name']}")
Model: my-model
Key measures: 20
First measure: Revenue Budget
Perfect for: Data engineers onboarding teams, consultants auditing models, organizations building AI copilots for BI.
v1.1.0 is published on PyPI under the name
pbi-context — this project was previously published as pbi-docs (v1.0.0–1.0.1); the tool
didn't change, only the name, to avoid a discoverability collision with an unrelated,
similarly-named project. See CHANGELOG.md
for the full rename note.
The read/context layer — PBIP/TMDL support, indexed output, query resolver, MCP server,
--diff-impact, --export-graph — is done, implemented and tested (see the Tests badge above for
the current count). Every claim above is backed by a dated, reproducible report, not just
asserted: see Validation below.
Next up: validating with real human users that scoped context doesn't cost time or accuracy versus raw file dumps — the protocol is ready (docs/human_validation_protocol.md), currently blocked on recruiting participants, not on code.
Writing/editing TMDL models and PBIR/report-layer parsing remain deliberately out of scope (see Roadmap for why).
Last updated: 2026-08-03.
Optional: virtual environment (venv). No external libraries required.
Just want to run pbi-context? pip install pbi-context (see Quick Start above) is all you need. The
steps below are for working on pbi-context itself (editable install from a local clone).
# 1) Clone or download the repository
# 2) (Optional) Create and activate a virtual environment
python -m venv venv
source venv/bin/activate
# 3) Editable installation (development)
pip install -e .
# Verify Python version
python --version
python -m venv venv
./venv/Scripts/Activate.ps1
If PowerShell blocks activation, run as Administrator:
Set-ExecutionPolicy -ExecutionPolicy RemoteSigned -Scope CurrentUser
Process a .pbit file:
pbi-context --input "data/pbit/my-model.pbit"
Process a PBIP project (new in v1.0):
# From the .pbip marker file
pbi-context --input "data/pbip/my-model.pbip"
# From the .SemanticModel folder directly
pbi-context --input "data/pbip/my-model.SemanticModel"
# From the project root folder (auto-detected)
pbi-context --input "data/pbip/my-model/"
Specify custom output directory:
pbi-context -i "data/pbit/my-model.pbit" -o "my-results"
Process multiple files (batch mode — mixed formats supported):
pbi-context --batch "data/pbit/*.pbit"
Compare two versions of a model (mixed .pbit/.pbip supported):
pbi-context --diff "data/pbit/model_v1.pbit" "data/pbip/model_v2/"
Verbose mode (more debugging information):
pbi-context --input "data/pbit/my-model.pbit" --verbose
Human-readable indexed output (indented JSON, for debugging — compact by default):
pbi-context --input "data/pbit/my-model.pbit" --pretty
Generate documentation in Spanish:
pbi-context --input "data/pbit/my-model.pbit" --lang es
Generate documentation in English (default):
pbi-context --input "data/pbit/my-model.pbit" --lang en
# Or simply omit --lang (English is the default)
pbi-context --input "data/pbit/my-model.pbit"
See docs/troubleshooting.md for the full expected-output walkthrough, how to verify a fresh install, and common errors.
.pbit files (ZIP + JSON TMSL) and .pbip projects (TMDL folder structure). .pbix files must be exported to .pbit from Power BI Desktop (File > Export > Power BI Template).--input flag accepts a .pbip marker file, a .SemanticModel/ folder, or a project root folder. Format is auto-detected.--lang en for English (default) or --lang es for Spanish. The language affects the generated model_documentation.md and agent_context.json files.data/ or data/pbit/ to keep the project organized.pbi-context/
├── pbi_extractor/ # Main package
│ ├── __init__.py
│ ├── cli.py # CLI with argparse (auto-detection, --index-format)
│ ├── extractor.py # .pbit (ZIP+JSON TMSL) extractor
│ ├── pbip_extractor.py # .pbip / TMDL extractor (new in v1.0)
│ ├── processor.py # Metadata processing (format-agnostic)
│ ├── indexed_output.py # index.json + tables/*.json writer (new in v1.0)
│ ├── formatters.py # Advanced hierarchical DAX formatting
│ ├── categorizer.py # Table/measure categorization
│ ├── documentation.py # Markdown generation
│ ├── diff.py # Model comparison
│ ├── jsonl_generator.py # JSONL generator for LLMs
│ └── i18n.py # Translations (en/es)
├── tests/
│ ├── fixtures/
│ │ └── minimal_pbip/ # TMDL test fixtures (new in v1.0)
│ ├── test_pbip_extractor.py
│ ├── test_cli_detection.py
│ ├── test_indexed_output.py
│ ├── test_categorizer.py
│ ├── test_i18n.py
│ └── test_processor_and_context.py
├── githooks/ # Reference pre-commit hook (docs/pre_commit_hook.md)
├── data/ # Input model files
├── output/ # Generated results
├── pyproject.toml # Package configuration
├── README.md
├── CHANGELOG.md
└── LICENSE
After running the command, a folder is created in output/ with the model name. Inside you'll find:
metadata.json
summary: totals of tables, visible columns, visible measures and relationships.tables: each table with columns (type, visibility, category) and measures (clean expression, format, display folder, category).relationships: from/to, cardinality, direction and active status.model_documentation.md
--lang flag, default: English).agent_context.json
--lang flag, default: English).model_context.jsonl
index.json (new in v1.0)
metadata.json.relationships.json (new in v1.0)
tables/<TableName>.json (new in v1.0)
index.json.index.json — open format specificationindex.json is a small, stable contract meant to be consumed directly by any tool — not just
pbi-context' own CLI/resolver/MCP server: a lightweight per-table summary (name, column/measure
counts, categories, format, and a relative path to that table's detail file) plus pointers to
every other output file, so an agent can navigate a large model without loading metadata.json.
By default it's written compact (no indentation); pass --pretty for indented JSON.
Full field-by-field contract — top-level shape, per-table entry, the tables/<Name>.json JSON vs.
TOON shapes, and the versioning policy — is documented in
docs/index-json-spec.md.
Problem: Your company has multiple undocumented Power BI dashboards. Analysts waste time searching for which measures to use and how tables are related.
Solution:
# Process all dashboards in a folder (English documentation)
pbi-context --batch "data/dashboards/*.pbit"
# Or generate Spanish documentation for all dashboards
pbi-context --batch "data/dashboards/*.pbit" --lang es
Result:
output/[dashboard-name].pbit/Output example:
output/
├── Sales Dashboard.pbit/
│ ├── model_documentation.md # 23 documented measures
│ └── metadata.json
├── Finance Dashboard.pbit/
│ ├── model_documentation.md # 31 documented measures
│ └── metadata.json
└── Operations Dashboard.pbit/
├── model_documentation.md # 18 documented measures
└── metadata.json
Problem: New employees need weeks to understand Power BI model structure and which measures to use for each analysis.
Solution:
# English documentation (default)
pbi-context --input "data/pbit/my-model.pbit"
# Spanish documentation
pbi-context --input "data/pbit/my-model.pbit" --lang es
Upload the model_documentation.md file to your favorite AI agent (Claude, GPT-4, etc.)
The agent can answer questions like:
Interaction example:
User: What revenue measures does this model have?
Agent: The "my-model" model has 11 revenue measures:
- Total Revenue (simple): SUM([Revenue])
- YTD Revenue (simple): TOTALYTD(SUM([Revenue]),'Date'[Date])
- Revenue SPLY (medium): CALCULATE([Total Revenue],SAMEPERIODLASTYEAR('Date'[Date]))
- Revenue Budget (medium): CALCULATE([Total Revenue], FILTER(Scenario, Scenario[Scenario]="Budget"))
...
Benefit: Significant reduction in onboarding time by having immediate answers about the model structure.
Problem: You need to compare two versions of the same dashboard to identify which measures or relationships changed between releases.
Solution:
# Compare two versions of the model
pbi-context --diff "data/pbit/dashboard_v1.pbit" "data/pbit/dashboard_v2.pbit"
Result: A diff_dashboard_v1_vs_dashboard_v2.json file is generated, content-aware — not just
which measures/columns/relationships were added or removed, but which existing ones changed
content (DAX expression, format string, display folder, hidden flag, category, data type,
cardinality, cross-filtering, active flag).
Identity for matching an object across both models:
(table, name).(from_table, from_column, to_table, to_column) — a relationship that keeps the
same connected columns but changes cardinality/cross-filtering/active flag shows up in
relationships_modified, not as a remove+add.DAX changes are flagged "semantic" or "cosmetic" via a whitespace-insensitive comparison of
formatted_expression — this is a text heuristic (DAX has no whitespace-sensitive syntax, so a
pure reindent compares equal), not a DAX parser; a change to a comment or to non-functional
casing would still register as semantic.
Output example (diff_*.json):
{
"a_model": "dashboard_v1",
"b_model": "dashboard_v2",
"measures_added": [["Fact", "New Revenue Measure"]],
"measures_removed": [["Fact", "Deprecated Measure"]],
"measures_modified": [
{
"table": "Fact",
"name": "Total Sales",
"changes": {
"formatted_expression": {"old": "SUM(Fact[Amount])", "new": "SUM(Fact[NetAmount])", "dax_change": "semantic"},
"display_folder": {"old": "", "new": "Sales"}
}
}
],
"columns_added": [],
"columns_removed": [],
"columns_modified": [
{"table": "Fact", "name": "Amount", "changes": {"data_type": {"old": "int64", "new": "decimal"}}}
],
"relationships_added": [],
"relationships_removed": [],
"relationships_modified": [
{
"from_table": "Fact", "from_column": "DateKey", "to_table": "Date", "to_column": "Date",
"changes": {"cardinality": {"old": "many:one", "new": "one:one"}}
}
]
}
Impact analysis (--diff-impact): add --diff-impact (optionally with --transitive) to
also report which measures reference each removed/modified measure or column — "what changed,
and what might break" in one call, connecting this diff to the resolver's find_measure_usages()
and find_column_usages():
pbi-context --diff "data/pbit/dashboard_v1.pbit" "data/pbit/dashboard_v2.pbit" --diff-impact --transitive
{
"measures_removed_impact": [
{"table": "Fact", "name": "Deprecated Measure", "used_by": [{"table": "Fact", "name": "Margin %"}]}
],
"measures_modified_impact": [
{"table": "Fact", "name": "Total Sales", "used_by": [{"table": "Fact", "name": "YTD Sales"}]}
],
"columns_removed_impact": [
{"table": "Fact", "name": "Discontinued Flag", "used_by": [{"table": "Fact", "name": "Active Sales"}]}
],
"columns_modified_impact": [
{"table": "Fact", "name": "Amount", "used_by": [{"table": "Fact", "name": "Total Sales"}]}
]
}
Removed measures/columns are checked for usages in the old model (those references just broke);
modified measures/columns are checked in the new model (those callers may now behave
differently). The same capability is exposed to AI agents as the diff_impact MCP tool (plus a
standalone find_column_usages tool) — see
MCP server in docs/use-cases.md.
Want this enforced automatically before a commit lands? See
docs/pre_commit_hook.md — a reference git pre-commit hook
(under githooks/) for repos that version .pbip/.pbit models, built on exactly
the command above.
More use cases — integrating with AI agents/RAG, chat-invocable Skills for Claude Code and
GitHub Copilot, the --query CLI, and the --mcp-serve MCP server — are in
docs/use-cases.md.
Every efficiency/correctness claim in this README is backed by a dated, reproducible report
against the real Supply Chain Sample.pbip fixture (not a synthetic toy model) — read these
before taking "AI-ready" or "token-optimized" at face value:
--query. JSON and
--query both scored 18/18; raw TMDL scored 16/18 (the 2 misses were honest NOT_FOUND on a
field TMDL doesn't contain at all, not agent error). Includes an honest caveat about total
conversation token overhead vs. raw context-source bytes.--index-format auto and the resolver still hold up, and is transparent about where a fixed
per-model cost (index.json) stops paying for itself at scale.The formatter now generates hierarchical indentation that reflects the logical structure of expressions:
CALCULATE([YTD Gross Margin],SAMEPERIODLASTYEAR(DATESYTD('Date'[Date])))
CALCULATE(
[YTD Gross Margin],
SAMEPERIODLASTYEAR(
DATESYTD(
'Date'[Date]
)
)
)
Formatting features:
Common errors, how to verify a fresh install, and installation issues are in docs/troubleshooting.md.
pbi-context is a read/context-compilation layer, a different category from Microsoft's own Power BI MCP servers (Modeling MCP for writes, Remote MCP for DAX execution) — complementary rather than competing.
Full example of a generated model_documentation.md:
docs/example_output.md.
pbi-context is a zero-dependency Python tool that turns a Power BI semantic model
(.pbit or the new .pbip/TMDL format) into documentation and structured context
that an AI agent can query — Markdown for humans, indexed JSON/JSONL for LLMs and RAG,
a query CLI, and a read-only MCP server.
Run pbi-context --input my-model.pbip to generate the context files, then either upload
model_documentation.md to your AI assistant, point an agent at the indexed JSON via
--query, or connect an agent directly through the read-only MCP server with --mcp-serve.
Yes. pbi-context ships a dedicated TMDL parser (not a regex over JSON) that reads .pbip
projects, .SemanticModel/ folders, and .pbit templates, auto-detecting the format.
Fabric uses the same TMDL format as PBIP, so compatibility is expected — but this has not yet been validated against a real Fabric export. See docs/fabric_compatibility.md.
Those are interactive desktop tools for editing and querying models. pbi-context is a
read-only, scriptable context layer: it never modifies your model, has zero .NET/desktop
dependencies, installs with pip, and produces LLM-ready output. It's complementary to
Microsoft's own Power BI MCP servers (Modeling MCP for writes, Remote MCP for DAX execution).
No. It is strictly read-only — it compiles context and audits changes, never authors or edits TMDL.
It exposes your processed model to an AI agent as queryable tools (list tables, get a table's measures, search measures, dependency/impact analysis, version diff) over the standard MCP protocol — so the agent pulls exactly what it needs instead of ingesting the whole model.
Yes, measurably — with the honest caveat that savings depend on model size and query type,
and can invert at large scale for some scenarios. The headline scenario totals in
docs/token_optimization_report.md are backed by a real
tokenizer (Gemini's count_tokens), not just estimated; some of its finer-grained,
table-by-table breakdowns and the 60-table scale test in
docs/scale_validation_report.md still use the standard
chars÷4 approximation, labeled as such everywhere it applies — see Validation
for the full picture, including where the approximation and the real count disagree.
Contributions are welcome! See CONTRIBUTING.md for the workflow, or use GitHub Issues for bug reports and feature requests.
Using it on a real model? A 5-minute report of what worked (or didn't) directly guides the roadmap → share your experience.
Done (v1.1.0, read/context layer): PBIP/TMDL parsing, indexed output (JSON/TOON), query
resolver + --query CLI, read-only MCP server, content-aware --diff with impact analysis
(measures and columns), --export-graph, a Mermaid ER diagram embedded in the generated docs.
Full history in CHANGELOG.md.
Next: validating with real human users (see Project Status above) — the one step between "the numbers look good" and "this actually helps people," and the signal that would justify expanding scope below.
Deliberately deferred, pending that signal:
Last updated: 2026-08-03.
The example files referenced in this documentation are official sample files provided by Microsoft. You can find these and other Power BI sample files in the Microsoft Power BI Desktop Samples repository.
These sample files are excellent for:
To use these samples:
git clone https://github.com/microsoft/powerbi-desktop-samples.git.pbix files in Power BI Desktop.pbit files (File > Export > Power BI Template)Oscar Rosero - Data Engineer | BI Developer
Built with ❤️ for the Power BI community.
If this tool saves you time, please consider giving it a ⭐ on GitHub!
AI context engine for Power BI models — turns .pbit/.pbip (TMDL) into indexed, queryable context for LLM agents (MCP server, CLI, docs). Zero dependencies.
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Pbi Context ทำงานแบบโลคัลผ่าน stdio
ตรวจสอบล่าสุดเมื่อ 2026-10-28 AI context engine for Power BI models — turns .pbit/.pbip (TMDL) into indexed, queryable context for LLM agents (MCP server, CLI, docs). Zero dependencies.