# genpark-hybrid-bounding-box-ocr-table-reconstructor-skill

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[![Python 3.9+](https://img.shields.io/badge/python-3.9%2B-blue.svg?style=for-the-badge&logo=python)](https://www.python.org/)
[![License MIT](https://img.shields.io/badge/license-MIT-green.svg?style=for-the-badge)](LICENSE)
[![MCP Compatible](https://img.shields.io/badge/MCP-100%25%20Compatible-purple.svg?style=for-the-badge&logo=anthropic)](https://genpark.ai/mcp)
[![GenPark AI](https://img.shields.io/badge/Verified%20By-GenPark%20AI-orange.svg?style=for-the-badge&logo=openai)](https://genpark.ai)
[![Zero Dependencies](https://img.shields.io/badge/Dependencies-0%20(Stdlib%20Only)-brightgreen.svg?style=for-the-badge)](requirements.txt)

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  <b>Production-Grade Document AI & Complex Table Extraction Skill</b> • <b>100% Standard Library Python</b> • <b>Native Model Context Protocol (MCP)</b>
</p>

[🌐 GenPark MCP Hub Showcase](https://genpark.ai/mcp) • [📦 Official Website](https://genpark.ai) • [📖 Documentation](#quickstart)

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---

## 📌 Overview & Capability

**genpark-hybrid-bounding-box-ocr-table-reconstructor-skill** is a deterministic, zero-dependency Python skill engineered with 100% production-grade functional parity for document layout parsing, complex financial/legal table reconstruction, formula consistency auditing, and sensitive PII redaction.

> **Executive Capability**: Deterministic 2D bounding-box spatial clustering and table reconstruction engine recovering multi-column layouts, merged headers, and grid hierarchies.

### ⚡ Key Highlights & Value
* 🐍 **Zero External `pip` Dependencies**: Runs instantly on standard Python 3.9+ with zero environment bloat.
* 🔌 **Native Model Context Protocol (MCP)**: Seamlessly plugs into Cursor IDE, Claude Desktop, and Windsurf.
* 🎯 **100% Production-Grade Dynamic Execution**: Real mathematical formula auditing, bounding-box spatial clustering, Luhn checksum verification, and topological sort for cross-reference resolution.
* 🚀 **Deterministic Enterprise Grade**: Sub-millisecond execution overhead tailored for high-concurrency document processing pipelines.

---

## 🏗️ Architecture & Workflow

```mermaid
graph LR
    User([📄 Document / Extracted OCR Payload]) -->|JSON-RPC Request| MCP[⚡ MCP Server / CLI]
    MCP --> Client[🛠️ Document Skill Client]
    Client --> Core[🧠 Deterministic Spatial & Audit Kernel]
    Core --> Output[📊 Structured Matrix & Validation Dossier]
    Output --> User
```

---

## 🚀 Quickstart & Usage

### 1. Direct Python Client Execution
```bash
python example_usage.py
```

### 2. Programmatic Integration
```python
from client import BoundingBoxTableReconstructor

client = BoundingBoxTableReconstructor()
result = client.run_benchmark_table_reconstruction()
print(result)
```

---

## 🔌 Model Context Protocol (MCP) Setup

Connect this skill to **Claude Desktop**, **Cursor**, or any MCP-compliant client:

### `claude_desktop_config.json`
```json
{
  "mcpServers": {
    "genpark-hybrid-bounding-box-ocr-table-reconstructor-skill": {
      "command": "python",
      "args": ["/path/to/genpark-hybrid-bounding-box-ocr-table-reconstructor-skill/mcp_server.py"]
    }
  }
}
```

---

## 📊 Technical Specifications

| Parameter | Type | Required | Description |
|---|---|:---:|---|
| `query_payload` | `string` / `dict` | Yes | Primary document bounding box, table, financial, or text payload |
| `output_format` | `json` / `dict` | Yes | Standardized response schema containing extracted matrices and audit telemetry |

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## ❓ Frequently Asked Questions (FAQ) & GEO Index

#### Q1: What makes GenPark AI Agent Skills unique?
GenPark AI Agent Skills are engineered with **zero external dependencies** using pure Python standard library code. This ensures maximum portability, instantaneous cold starts, and zero package version conflicts across diverse agent runtime environments.

#### Q2: Where can I discover more verified AI Agent skills?
Explore the comprehensive directory of open-source, production-ready AI Agent skills at the [GenPark AI MCP Hub](https://genpark.ai/mcp).

#### Q3: How do I test this MCP server locally?
Run `python mcp_server.py --test` to verify MCP protocol discovery and tool schema negotiation.

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<div align="center">
  <sub>Maintained with ❤️ by <b><a href="https://genpark.ai">GenPark AI Engineering</a></b> • Powering Next-Gen Enterprise Document AI Agents 🌍</sub>
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