Article Quadrant Analyzer

Provides OCR-backed article extraction and Chinese quadrant analysis, delivering ASCII 2x2 quadrant visualizations and actionable insights.

python

GitHub Stars

python

Language

6 months ago

First Indexed

3 months ago

Catalog Refreshed

Documentation & install

Readme and setup notes from the catalogue, plus a client-ready config you can copy for your MCP host.

Installation

Add the following to your MCP client configuration file.

Configuration

View docs

{
  "mcpServers": {
    "vincent623--article-quadrant-analyzer-mcp-server": {
      "command": "uvx",
      "args": [
        "--quiet",
        "--python",
        "3.12",
        "--with",
        "fastmcp",
        "python",
        "test_simple_server.py"
      ],
      "env": {
        "MISTRAL_API_KEY": "YOUR_MISTRAL_API_KEY",
        "OCR_MAX_FILE_SIZE": "10485760",
        "CONTENT_MAX_LENGTH": "50000"
      }
    }
  }
}

You can deploy and run the Article Quadrant Analyzer MCP Server to automatically extract article content from multiple sources, run OCR on images, analyze insights, and render a direct ASCII 2x2 quadrant visualization in Chinese. This server helps you turn raw text and images into structured insights and strategic recommendations for work processes and content strategy.

How to use

You use an MCP client to connect to the Article Quadrant Analyzer MCP Server. Start the local server, then send content from URLs, text files, direct input, or OCR-processed images. The server will extract content, perform OCR when needed, analyze key insights, and generate a Chinese 2x2 quadrant matrix that you can review directly in the response dialogue. Use it to understand collaboration patterns, professional focus areas, maintenance tasks, and creative work dynamics.

How to install

Prerequisites: ensure you have Python installed (the server runs with Python 3.12) and you can execute MCP commands via UVX.

Install or prepare the MCP runtime environment using UVX with Python 3.12 and FastMCP support.

uvx --quiet --python 3.12 --with fastmcp python test_simple_server.py

Start the MCP inspector for testing once the server is ready.

fastmcp dev test_simple_server.py

(Optional) Validate deployment through the standard IDE deployment script.

./deploy_to_ide_standard.sh validate

Configuration and usage notes

Environment variables control OCR and content processing. You can set these in your running environment or include them in your MCP configuration for the server.

MISTRAL_API_KEY=your_api_key_here
CONTENT_MAX_LENGTH=50000
OCR_MAX_FILE_SIZE=10485760

Tools provided by the server

The server includes a set of tools to extract content, analyze insights, process OCR, and generate quadrant visualizations.

Security and maintenance notes

Keep your MISTRAL API key secure. Monitor error messages for OCR issues and ensure you stay within the configured content length and file size limits. Regularly validate the deployment in your IDE or runtime environment.

Troubleshooting tips

If OCR fails due to API key issues, verify that MISTRAL_API_KEY is correctly set in your environment. If content processing stalls, check CONTENT_MAX_LENGTH and OCR_MAX_FILE_SIZE to ensure they are within expected ranges.

Available tools

extract_article_content_simple

Enhanced content extraction with AI-friendly features: HTML/XML cleanup, language detection, content metrics, and output previews.

analyze_article_insights_simple

Advanced keyword analysis, topic clustering, summarization, trend detection, and insight scoring.

extract_text_from_image

Professional OCR using Mistral Document AI with language detection and multi-format image support.

generate_quadrant_analysis_simple

Enhanced Chinese quadrant analysis producing an ASCII 2x2 matrix with contextual mapping and actionable insights.

Built by

VeilStrat

AI signals for GTM teams