context-renderer 0.1.3

Generate structured Markdown context sheets from MSSQL databases

context-renderer

Generate structured Markdown context sheets from your MSSQL databases — in one command.

context-renderer connects to a Microsoft SQL Server database, inspects its full structure, computes data metrics, and produces clean Markdown context sheets — one overview + one detailed file per table.

Perfect for onboarding, documentation, LLM context injection, or just understanding an unfamiliar database.

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Installation

pip install context-renderer

Prerequisite: ODBC Driver 18 for SQL Server must be installed on the host machine.

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Usage

from context_renderer import MSSQLConnector, DatabaseInspector, DataMetrics, ContextSheetRenderer

# 1. Connect
connector = MSSQLConnector(host="your-server", database="your-db", username="sa", password="...")
engine = connector.get_engine()

# 2. Inspect schema
schema = DatabaseInspector(engine).inspect()

# 3. Compute data metrics (optional — skip for speed)
metrics = DataMetrics(engine).compute_all(schema.tables)

# 4. Save context sheets
ContextSheetRenderer(schema, metrics).save("./output")

Output structure

output/your-db/
├── 00_database_overview.md     ← Summary, table list, FK relation map, routines
├── dbo__Users.md               ← Columns, PKs, FKs, indexes, null %, cardinality, top values
├── dbo__Orders.md
└── ...

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What the sheets look like

00_database_overview.md

# Database Overview — `MyDatabase`

## Summary
| Property | Value          |
|----------|----------------|
| Schemas  | `dbo`, `audit` |
| Tables   | 12             |
| Views    | 3              |

## Foreign Key Relations
- `dbo.Orders.user_id` → `dbo.Users.id`
- `dbo.OrderItems.order_id` → `dbo.Orders.id`

dbo__Orders.md

# `dbo`.`Orders`
> Row count: 1,482,309

## Columns
| # | Column   | Type          | Nullable | PK | Null % | Distinct | Top Values        |
|---|----------|---------------|----------|----|--------|----------|-------------------|
| 1 | `id`     | `int`         | ✗        | 🔑 | 0.0%   | 1482309  | —                 |
| 2 | `status` | `varchar(20)` | ✗        |    | 0.0%   | 4        | `pending`, `paid` |
| 3 | `amount` | `decimal`     | ✓        |    | 2.1%   | 18402    | —                 |

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Full project & Jupyter notebook

For a Docker-based Jupyter workflow (no local ODBC setup required), see the project repository.

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License

MIT — see LICENSE.