Skip to main content
Glama
alphaparkinc

genpark-agent-multimodal-document-table-extractor-skill

Official

genpark-agent-multimodal-document-table-extractor-skill

Python 3.9+ License MIT MCP Compatible GenPark AI Zero Dependencies

🌐 GenPark MCP Hub • 📦 GenPark Official • 📖 Documentation


📌 Overview & Capability

genpark-agent-multimodal-document-table-extractor-skill is a deterministic, high-performance, zero-dependency Python tool and native Model Context Protocol (MCP) server designed for autonomous AI agents, multi-agent frameworks (LangGraph, CrewAI, AutoGen, OpenAI Swarm), and developer environments (Cursor, Windsurf, Claude Desktop).

Executive Capability: Heuristic multi-column document and tabular markdown extractor converting ASCII/OCR text dumps, aligned spacing, and CSV/TSV into clean GitHub Flavored Markdown and typed JSON.

⚡ Key Highlights

  • 🐍 Zero External pip Dependencies: Implemented entirely with pure Python standard library for instant zero-overhead execution.

  • 🔌 Native Model Context Protocol (MCP): Plugs directly into any MCP-compliant client via JSON-RPC 2.0 stdio.

  • ⚡ Sub-Millisecond Execution: Slashes token burn and latency by resolving routine agent tasks deterministically without frontier LLM round-trips.

  • 🛡️ Production-Hardened: Comprehensive error handling, boundary validation, and telemetry.


Related MCP server: genpark-multimodal-vision-table-structural-extractor-skill

🏗️ Architecture

graph LR
    Agent([🤖 Autonomous Agent / IDE]) -->|MCP Protocol / JSON-RPC| Server[⚡ genpark-agent-multimodal-document-table-extractor-skill Server]
    Server --> Core[🧠 Deterministic Processing Core]
    Core --> Out[📊 Actionable Result & Telemetry]
    Out --> Agent

🚀 Quickstart & Usage

1. Direct Python Client Execution

python example_usage.py

2. Programmatic Integration

from client import AgentDocumentTableExtractor

client = AgentDocumentTableExtractor()
result = client.run_table_benchmark()
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-agent-multimodal-document-table-extractor-skill": {
      "command": "python",
      "args": ["/path/to/genpark-agent-multimodal-document-table-extractor-skill/mcp_server.py"]
    }
  }
}

Direct MCP Testing

python mcp_server.py --test

📊 Technical Specifications

Parameter

Type

Required

Description

payload

string / dict

Yes

Primary context, code, schema, or content input

options

dict

No

Execution flags, compression ratios, or risk bounds


Maintenance

ActivityMaintained
ResponsivenessNo issues

Related MCP Connectors

Related MCP Servers

  • A
    license
    Not graded
    quality
    D
    maintenance
    Bidirectional CSV ↔️ JSON ↔️ Markdown transformer exposed as an HTTP-based MCP server, enabling format conversion through natural language.
    20 npm
    MIT
  • F
    license
    Not graded
    quality
    B
    maintenance
    Enables multimodal OCR-based extraction of table grids and merged cells into Markdown with deterministic, zero-dependency execution through MCP for AI agents.
    9
    -
  • A
    license
    A
    quality
    C
    maintenance
    Converts, aligns, reshapes, and inspects tabular data across Markdown, CSV, TSV, JSON, and HTML with CJK-aware alignment and deterministic transformations, entirely offline and without file access.
    4
    MIT
  • A
    license
    Not graded
    quality
    B
    maintenance
    Enables converting Markdown and HTML tables into structured JSON records with automatic column type inference and format normalization. It also offers supporting web-agent primitives such as DOM semantic pruning, form field auto-mapping, and execution trajectory evaluation, all via a zero-dependency MCP server.
    7
    MIT