GenPark Hybrid Bounding Box OCR Table Reconstructor
LocalAbout GenPark Hybrid Bounding Box OCR Table Reconstructor
A Python MCP server that turns OCR bounding boxes into structured tables using deterministic spatial clustering. It is intended for document-processing workflows and also supports formula checks, cross-reference resolution, and PII redaction.
Key features
- 01OCR bounding-box spatial clustering
- 02Reconstruction of table grids and merged headers
- 03Formula consistency auditing
- 04Cross-reference resolution
- 05PII redaction
Use cases
- 01Rebuilding tables from OCR output
- 02Auditing formulas in financial or legal tables
- 03Redacting sensitive PII from document data
MCP support
Exposes document-layout reconstruction and auditing capabilities through an MCP server for MCP-compliant clients, including Claude Desktop and Cursor.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project READMEgenpark-hybrid-bounding-box-ocr-table-reconstructor-skill
Production-Grade Document AI & Complex Table Extraction Skill • 100% Standard Library Python • Native Model Context Protocol (MCP)
🌐 GenPark MCP Hub Showcase • 📦 Official Website • 📖 Documentation
📌 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
pipDependencies: 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
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
python example_usage.py
2. Programmatic Integration
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
{
"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 |
❓ 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.
Q3: How do I test this MCP server locally?
Run python mcp_server.py --test to verify MCP protocol discovery and tool schema negotiation.
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.