GenPark Multimodal Chart Data Point Extractor

io.github.alphaparkincPublished by GitHub account alphaparkincv1.0.1Updated Nov 2, 2026

Calibrate chart axes and convert bar or scatter pixel coordinates to data values

VerifiedSTDIODesktop onlyData & Analytics Media & Design

Server details

Identifier: io.github.alphaparkinc/genpark-multimodal-chart-data-point-extractor-skill
Transport: STDIO
Runtime: Desktop only
Trust: Verified
Published: Oct 19, 2026

Security note

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Overview

AI-generated overview

Local Python MCP server that calibrates chart axes and converts bar or scatter pixel coordinates into numeric data values.

What you can do: Calibrate chart axes from a supplied payload
Convert bar, line and scatter pixel coordinates into structured numeric values
Return extracted matrices and audit telemetry in a standardized JSON response
Run as a local MCP server or as a standalone Python client script
When to use it: Useful when you have chart images or OCR payloads with pixel coordinates and want the underlying numbers recovered as structured data, for example turning a bar or scatter chart into a table.
Requirements: Runs as a local process on the user's machine (desktop only) using Python 3.9+ with no external pip dependencies; the README shows launching mcp_server.py with the python command. No accounts, API keys or environment variables are declared.

Before you install

The README describes a much broader scope (table reconstruction, formula auditing, PII redaction) than the registry description, so verify the actual tool set before relying on it. Chart payloads you submit are processed by the local process; no third-party data transfer is documented.

Installation

In SourceWeft

Open GenPark Multimodal Chart Data Point Extractor in the dashboard and add it to a workspace.
Enable the server for the chats that should use its tools.

Desktop only via STDIO. STDIO servers start a local process, so they need the SourceWeft desktop host.

Other MCP clients

Follow the launch instructions in the repository.

README

genpark-multimodal-chart-data-point-extractor-skill

[Python 3.9+] [License MIT] [MCP Compatible] [GenPark AI] [Zero Dependencies]

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-multimodal-chart-data-point-extractor-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: Multimodal chart coordinate calibration and tabular dataset recovery engine transforming visual bar, line, and scatter charts into structured numbers.

⚡ 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

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

2. Programmatic Integration

python

from client import MultimodalChartDataPointExtractor
client = MultimodalChartDataPointExtractor()result = client.run_benchmark_chart_extraction()print(result)

from client import MultimodalChartDataPointExtractor
client = MultimodalChartDataPointExtractor()result = client.run_benchmark_chart_extraction()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-multimodal-chart-data-point-extractor-skill": {      "command": "python",      "args": ["/path/to/genpark-multimodal-chart-data-point-extractor-skill/mcp_server.py"]    }  }}

{  "mcpServers": {    "genpark-multimodal-chart-data-point-extractor-skill": {      "command": "python",      "args": ["/path/to/genpark-multimodal-chart-data-point-extractor-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.

_{Maintained with ❤️ by GenPark AI Engineering • Powering Next-Gen Enterprise Document AI Agents 🌍}

Source: README.md at commit 73b12e3

Tools

SourceWeft has not started this package in its sandbox yet, so its tools are not known.

Version history

v1.0.1LatestOct 19, 2026

Score

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