pbi-context 1.1.0

AI context engine for Power BI models: indexed, queryable context for LLM agents (MCP server, CLI, docs), zero dependencies

pbi-context — AI Context Engine for Power BI Models

pbi-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.

Quick Start

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.

Full walkthrough — folder structure, expected output, and using the context in Python

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

Why pbi-context?

Your Need	pbi-context Solution
Document 10+ dashboards fast	Batch processing with --batch
Support the new PBIP format	Full TMDL parser, auto-detected from .pbip or folder
Train AI agents on your models	Indexed JSON/JSONL context, a query CLI, and an MCP server
Let an AI agent query the model live	Read-only MCP server (--mcp-serve) — validated against a test harness, not yet a live MCP client, see MCP server
Use it from your AI coding assistant	Chat-invocable Skill for Claude Code + prompt file for GitHub Copilot
Actually readable DAX	Hierarchical indentation (4x better than raw)
Compare model versions	Content-aware --diff, with impact analysis (--diff-impact)
See the model at a glance	Embedded Mermaid ER diagram in model_documentation.md — renders natively on GitHub/VS Code
Visualize the model in Gephi/yEd	--export-graph — JSON node/edge lists or GraphML
Zero-cost, zero-install	Python-only, no .NET dependencies

Perfect for: Data engineers onboarding teams, consultants auditing models, organizations building AI copilots for BI.

Project Status

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.

Requirements

• Python 3.10+ (3.12 recommended)
• Works on Windows, macOS, and Linux

Optional: virtual environment (venv). No external libraries required.

Installation

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

Windows/PowerShell notes

python -m venv venv
./venv/Scripts/Activate.ps1

If PowerShell blocks activation, run as Administrator:

Set-ExecutionPolicy -ExecutionPolicy RemoteSigned -Scope CurrentUser

---

Quick Usage

Basic Commands

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.

Important Notes

• Supported formats: .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).
• PBIP entry points: The --input flag accepts a .pbip marker file, a .SemanticModel/ folder, or a project root folder. Format is auto-detected.
• Microsoft Fabric semantic models: Fabric uses the same TMDL format as PBIP, so compatibility is expected but not empirically validated (no real Fabric export has been tested against this parser yet) — see docs/fabric_compatibility.md.
• Language selection: Use --lang en for English (default) or --lang es for Spanish. The language affects the generated model_documentation.md and agent_context.json files.
• Paths with spaces: Use quotes around paths that contain spaces.
• Recommended paths: Place your files in data/ or data/pbit/ to keep the project organized.

---

Project Structure

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

---

Generated Outputs

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

  • Model summary (language depends on --lang flag, default: English).
  • List of tables (hidden or business), visible columns and measures grouped by category: revenue, cost, margin, percentage, ratio, temporal, etc.
  • Sections with DAX expressions formatted with hierarchical indentation.
  • Relationships table with visual representation of table connections.
  • AI Agent Usage Guide with sample questions (translated based on selected language).

• agent_context.json

  • Model name, totals, available tables, key measures (up to 20), temporal columns and sample questions (language depends on --lang flag, default: English).

• model_context.jsonl

  • Line-delimited JSON format optimized for embeddings and RAG.
  • Each line is an independent object (table, measure or relationship).
  • Includes formatted DAX and sample prompts.

• index.json (new in v1.0)

  • Lightweight model summary with per-table metadata (column/measure counts, categories) and relative paths to all other output files.
  • Allows LLM agents to navigate large models without loading the full metadata.json.

• relationships.json (new in v1.0)

  • All model relationships in a single focused file.

• tables/<TableName>.json (new in v1.0)

  • Full detail for one table (columns + measures including DAX).
  • One file per table, addressable via index.json.

---

index.json — open format specification

index.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.

---

Use Cases

1. Automatic Dashboard Documentation

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:

• Each dashboard generates its own documentation in output/[dashboard-name].pbit/
• Documentation ready to share with the team
• Automatic identification of measures by category (revenue, cost, margin, etc.)

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

---

2. New Analyst Onboarding

Problem: New employees need weeks to understand Power BI model structure and which measures to use for each analysis.

Solution:

1. Generate the model documentation (in your preferred language):

# English documentation (default)
pbi-context --input "data/pbit/my-model.pbit"

# Spanish documentation
pbi-context --input "data/pbit/my-model.pbit" --lang es

2. Upload the model_documentation.md file to your favorite AI agent (Claude, GPT-4, etc.)

3. The agent can answer questions like:

   • "What revenue measures are available?"
   • "How is Gross Margin calculated?"
   • "What tables are related to Customer?"

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.

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3. Model Auditing

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:

• Measures and columns: (table, name).
• Relationships: (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.

---

Validation

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:

• docs/pbip_validation_report.md — end-to-end validation of PBIP/TMDL extraction and both output formats against a real (non-synthetic) export; documents 5 bugs found and fixed in the process.
• docs/token_optimization_report.md — measured token cost of raw TMDL vs pbi-context JSON vs TOON across 3 usage scenarios, plus a table-by-table breakdown showing TOON is not a uniform win (loses on small tables).
• docs/precision_validation_report.md — automated proxy for the "does scoped context sacrifice accuracy?" question: 3 isolated agents answer 18 objectively-gradable questions using only raw TMDL / only JSON / only --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.
• docs/scale_validation_report.md — behavior at 60 tables/288 measures (synthetic, since no public enterprise-scale PBIP model exists): confirms --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.
• docs/human_validation_protocol.md (protocol — not yet executed) — the planned human-subject experiment for validating that scoped context doesn't cost real users time or accuracy versus raw file dumps.

---

DAX Formatting Example

The formatter now generates hierarchical indentation that reflects the logical structure of expressions:

Before (unformatted):

CALCULATE([YTD Gross Margin],SAMEPERIODLASTYEAR(DATESYTD('Date'[Date])))

After (hierarchical formatting):

CALCULATE(
    [YTD Gross Margin],
    SAMEPERIODLASTYEAR(
        DATESYTD(
            'Date'[Date]
        )
    )
)

Formatting features:

• Each nesting level increases indentation (+4 spaces)
• Closing parentheses aligned with the start of their function
• Arguments on separate lines for clarity
• Preservation of all original arguments
• Complexity indicators (Simple / Medium / Complex)

---

Troubleshooting

Common errors, how to verify a fresh install, and installation issues are in docs/troubleshooting.md.

---

Comparison with Alternatives

Feature	pbi-context	Power BI Helper	Dataedo	Manual (DAX Studio)
Free & Open Source	Yes	No (Paid)	No (Paid)	Yes
Batch Processing	Yes - Multiple files	No - One at a time	Yes	No
Multi-language Support	Yes - English & Spanish	No	No	No
AI-Ready Outputs	Yes - JSON + JSONL	No	Limited	No
Hierarchical DAX	Yes	Basic	Basic	No - Raw only
No Installation	Yes - pip install	Limited - Desktop app	Limited - Platform	Yes
Categorization	Yes - Auto (revenue, cost...)	No - Manual	Yes	No
Version Diff	Yes - Built-in	No	Yes	No - Manual
MCP Server / On-demand Query	Yes - Built-in, zero deps	No	No	No

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.

---

Generated Documentation Example

Full example of a generated model_documentation.md: docs/example_output.md.

---

FAQ

What is pbi-context?

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.

How do I give Claude, Copilot, or ChatGPT context about my Power BI model?

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.

Does pbi-context support the new PBIP / TMDL format?

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.

Does it work with Microsoft Fabric semantic models?

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.

How is pbi-context different from Tabular Editor or DAX Studio?

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).

Does pbi-context modify my model?

No. It is strictly read-only — it compiles context and audits changes, never authors or edits TMDL.

What does the MCP server do?

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.

Does it reduce token usage?

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.

---

Contributing

Contributions are welcome! See CONTRIBUTING.md for the workflow, or use GitHub Issues for bug reports and feature requests.

---

Help shape pbi-context

Using it on a real model? A 5-minute report of what worked (or didn't) directly guides the roadmap → share your experience.

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Roadmap

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:

• Writing/editing TMDL models — safely writing TMDL back (preserving formatting, comments, lineage tags, merges) is substantially riskier than reading, and Microsoft's own Modeling MCP already covers that space.
• PBIR/report-layer parsing (pages, visuals, bookmarks) — a different problem from documenting the data model, out of scope for now.

Last updated: 2026-08-03.

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Sample Files

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:

• Testing pbi-context functionality
• Learning Power BI data modeling
• Exploring different DAX patterns and measure types
• Understanding relationship structures

To use these samples:

1. Clone or download the repository: git clone https://github.com/microsoft/powerbi-desktop-samples.git
2. Open the .pbix files in Power BI Desktop
3. Export them as .pbit files (File > Export > Power BI Template)
4. Use them with pbi-context to generate documentation

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Author

Oscar Rosero - Data Engineer | BI Developer

• LinkedIn
• GitHub
• [e-mail]

Built with ❤️ for the Power BI community.

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