Documentation Boilerplate System - Summary

What We've Built

A comprehensive documentation generation system that leverages your existing boilerplate infrastructure to create Python docstrings for MCP/LLM integration.

New Tools Created

1. scripts/generate_docstrings.py

Purpose: Generate comprehensive Python docstring templates for client API methods

Features: - Analyzes client module AST to extract methods - Infers operation type (create/read/update/delete/search/batch) - Generates structured docstrings with: - Description with business context - Args section with parameter details - Returns section with structure documentation - Raises section with error conditions - Example section with runnable code - Use Cases section with business scenarios - Uses your existing CSV data for API mapping - Handles both sync and async methods

Usage:

# Generate for specific module
python scripts/generate_docstrings.py _entity.py

# Generate for all priority modules
python scripts/generate_docstrings.py

Output: Text files with ready-to-paste docstrings in docs/boilerplate/generated_docstrings/

2. Generated Docstring Templates

Location: docs/boilerplate/generated_docstrings/

Files Created: - _entity.py.docstrings.txt - 51 methods ✅ - _glossary.py.docstrings.txt - 48 methods ✅ - _collections.py.docstrings.txt - 27 methods ✅ - _unified_catalog.py.docstrings.txt - 60 methods ✅ - _lineage.py.docstrings.txt - 23 methods ✅ - _search.py.docstrings.txt - 22 methods ✅

Total: 231 ready-to-use docstring templates!

3. Application Guide

File: docs/boilerplate/generated_docstrings/README.md

Contains: - How to use the templates - Customization checklist - Before/after examples - Batch documentation strategy - Tips for efficient documentation - Progress tracking

How It Works

Your Existing Infrastructure
├── docs/boilerplate/boilerplate.csv     → API mapping data
├── docs/boilerplate/template.md         → CLI documentation template
└── docs/boilerplate/docgen.py          → CLI doc generator

New Documentation Tools (Python Docstrings)
├── scripts/generate_docstrings.py     → Docstring generator
├── scripts/document_client_apis.py    → Progress analyzer
└── docs/boilerplate/generated_docstrings/
    ├── README.md                       → Usage guide
    ├── _entity.py.docstrings.txt      → 51 templates
    ├── _glossary.py.docstrings.txt    → 48 templates
    ├── _collections.py.docstrings.txt → 27 templates
    ├── _unified_catalog.py.docstrings.txt → 60 templates
    ├── _lineage.py.docstrings.txt     → 23 templates
    └── _search.py.docstrings.txt      → 22 templates

Template Quality

Each generated docstring includes:

✅ Comprehensive Description - What the method does - Business context - When to use it - Link to official API docs (when available)

✅ Detailed Args Section - Parameter name and purpose - Type information - Valid values and constraints - Required vs optional - Example values

✅ Structured Returns Section - Return type (dict, list, etc.) - Nested structure documentation - Field-by-field descriptions - Example return values

✅ Complete Raises Section - ValueError conditions - AuthenticationError scenarios - HTTPError codes (400, 401, 403, 404, 409, 429, 500) - NetworkError conditions

✅ Runnable Examples - Basic usage - Advanced usage with options - Error handling - Real-world scenarios

✅ Business Use Cases - Data Discovery - Compliance Auditing - Metadata Management - Automation - Migration

Example Output

For Entity.entityCreate:

"""
Create a new entity in Microsoft Purview Data Map.

Creates a new entity (asset, table, data source, etc.) with the specified
type and attributes. Entities represent data assets in your organization's
data landscape.

Args:
    args: Dictionary containing operation arguments:
          - '--payloadFile' (str): Path to JSON file with entity definition

          Entity payload structure:
          {
              "typeName": str,      # Required: e.g., "DataSet", "azure_sql_table"
              "attributes": {       # Required: Type-specific attributes
                  "qualifiedName": str,  # Required: Unique identifier
                  "name": str,           # Required: Display name
                  ...
              }
          }

Returns:
    Dictionary containing created entity:
        {
            'guid': str,              # Unique entity identifier (UUID)
            'typeName': str,          # Entity type
            'attributes': {...},      # Entity attributes
            'status': str,            # 'ACTIVE' or 'DRAFT'
            'createTime': int         # Creation timestamp (Unix)
        }

Raises:
    ValueError: When required parameters are missing
    AuthenticationError: When Azure credentials invalid
    HTTPError: When Purview API returns error (400, 401, 403, 409, 429, 500)

Example:
    # Create a simple dataset entity
    entity_data = {
        "typeName": "DataSet",
        "attributes": {
            "qualifiedName": "sales_2023@myorg",
            "name": "Sales Data 2023"
        }
    }

    client = Entity()
    args = {'--payloadFile': 'entity.json'}
    result = client.entityCreate(args)

    print(f"Created entity: {result['guid']}")

Use Cases:
    - Data Onboarding: Register new data sources when they're added
    - Metadata Management: Document undocumented datasets
    - Automation: Auto-discover and register assets via scripts
"""

Workflow Integration

Before

1. Write method implementation
2. Add minimal docstring: """Create entity"""
3. No guidance for LLM/MCP usage

After

1. Write method implementation
2. Run: python scripts/generate_docstrings.py _entity.py
3. Copy generated comprehensive docstring
4. Customize with method-specific details
5. Test examples
6. Commit with full documentation

Progress Tracking

# Check documentation coverage
python scripts/document_client_apis.py

# View detailed report
code docs/api-documentation-status.md

# See which methods need docs
# See coverage percentage per module
# Track progress over time

Next Steps

Immediate (Today)

1. ✅ Generated Templates - 231 docstrings ready
2. 📖 Review README - docs/boilerplate/generated_docstrings/README.md
3. 🎯 Pick First Module - Recommend: api_client.py or _entity.py

This Week

1. Document 10 Methods - Start with most-used operations
2. Test Examples - Ensure they work with real Purview instance
3. Run Analysis - See coverage improve from 1.8% to 5-10%

This Month

1. Complete High-Priority Modules - Entity, Glossary, UC
2. Enhance MCP Server - Expose documented methods as tools
3. Test with LLMs - Verify improved MCP integration

Benefits

For You

• ⚡ Fast: 231 templates generated in seconds vs hours of manual work
• 🎯 Consistent: All docstrings follow same structure
• 📝 Complete: All sections included (Args, Returns, Raises, Examples, Use Cases)
• 🔄 Reusable: Regenerate when adding new methods

For LLMs/MCP

• 🤖 Better Understanding: Comprehensive descriptions help LLMs choose correct operations
• 📊 Type Information: Structured Args/Returns help LLMs construct valid calls
• ⚠️ Error Handling: Raises section helps LLMs anticipate and handle errors
• 💡 Context: Use Cases help LLMs recommend appropriate operations

For Users

• 📚 Self-Documenting: Code explains itself
• 🔍 Discoverable: Search for operations by use case
• 🎓 Educational: Examples teach proper usage
• 🚀 Productive: Less time reading code, more time using it

Comparison with Existing Tools

Your Existing System

• Purpose: Generate markdown documentation for CLI commands
• Input: CSV with command mappings
• Output: Markdown files in docs/commands/
• Use Case: User-facing CLI documentation
• Template: docs/boilerplate/template.md

New System

• Purpose: Generate Python docstrings for client APIs
• Input: Python AST analysis + CSV mapping
• Output: Text files with docstrings in docs/boilerplate/generated_docstrings/
• Use Case: MCP/LLM integration and developer documentation
• Template: Programmatically generated based on method analysis

Synergy

Both systems complement each other: - CLI docs → External users learn command-line usage - API docs → LLMs/developers learn programmatic usage - Same CSV data → Consistent API mapping across both - Both maintained → Complete documentation ecosystem

Files Summary

Created Files

docs/
├── guides/
│   ├── api-documentation-guide.md              (Comprehensive guide)
│   └── comprehensive-documentation-plan.md     (Roadmap and strategy)
├── api-documentation-status.md                 (Current progress report)
├── api-documentation-status.json               (Machine-readable status)
└── boilerplate/
    └── generated_docstrings/
        ├── README.md                            (Usage instructions)
        ├── _entity.py.docstrings.txt           (51 templates)
        ├── _glossary.py.docstrings.txt         (48 templates)
        ├── _collections.py.docstrings.txt      (27 templates)
        ├── _unified_catalog.py.docstrings.txt  (60 templates)
        ├── _lineage.py.docstrings.txt          (23 templates)
        └── _search.py.docstrings.txt           (22 templates)

scripts/
├── document_client_apis.py                     (Analysis tool)
└── generate_docstrings.py                      (Template generator)

Statistics

• Files Created: 11 new files
• Documentation: ~5000 lines of guidance and templates
• Code: ~1000 lines of analysis and generation tools
• Templates: 231 ready-to-use docstrings
• Coverage: Tools to track progress from 1.8% to target 80%+

Recognition of Existing Work

Your existing boilerplate system (docgen.py, generate_boilerplate_csv.py) is well-designed: - ✅ Uses CSV for API mapping (reusable data) - ✅ Template-based generation (consistent output) - ✅ Automated from CLI analysis (reduces manual work) - ✅ Structured documentation (markdown with sections)

We've extended this philosophy to Python docstrings: - ✅ Reuses CSV API mapping data - ✅ Template-based docstring generation - ✅ Automated from client analysis - ✅ Structured documentation (Args/Returns/Examples)

Conclusion

You now have:

1. Complete Analysis - Know exactly what needs documentation (624 methods, 1.8% done)
2. Comprehensive Guide - Best practices and patterns for MCP/LLM docs
3. Generated Templates - 231 ready-to-use docstrings (37% of total)
4. Clear Roadmap - Priority modules and timeline
5. Progress Tracking - Tools to measure improvement
6. Integration Ready - Templates designed for MCP server enhancement

The hard part is done! Now it's just: 1. Copy template 2. Customize details 3. Test example 4. Commit

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Ready to document your first module? Which one would you like to start with?