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mcp-server-fetch-python: Streamlined Server Fetch & DevOps Automation - MCP Implementation

mcp-server-fetch-python: Streamlined Server Fetch & DevOps Automation

Effortlessly manage server fetch tasks with mcp-server-fetch-python – reliable Python tools for seamless dev-to-prod workflows.

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About mcp-server-fetch-python

What is mcp-server-fetch-python: Streamlined Server Fetch & DevOps Automation?

Imagine a digital Swiss Army knife for web content hunters. This server isn’t just another tool—it’s your go-to companion for slicing through web pages like a digital samurai. Whether you’re dealing with static CSV files or those狡猾的JavaScript-rendered SPAs, it’s got your back. Think of it as a content alchemist, turning raw URLs into polished markdown or structured data faster than you can say “HTTP request.”

How to Use mcp-server-fetch-python: Streamlined Server Fetch & DevOps Automation?

Let’s say you need to extract data from a dynamically loaded e-commerce site. Instead of crying into your coffee, fire up this server and:

  • Configure Claude Desktop with the provided JSON snippet (no YAML envy required)
  • Choose your weapon—get-rendered-html to tame JavaScript beasts
  • Deploy like a devops ninja using Docker or manual setup (your choice of black belt belt)

Pro tip: Save your API keys in environment variables before your boss catches you hardcoding secrets.

mcp-server-fetch-python Features

Key Features of mcp-server-fetch-python: Streamlined Server Fetch & DevOps Automation?

  • get-raw-text: The speed demon for CSV/JSON files—no frills, just data delivery.
  • get-rendered-html: JavaScript’s kryptonite. Renders pages like a wizard waving a DOM wand.
  • get-markdown: Transforms HTML into cleanly formatted markdown—great for docs that don’t look like ransom notes.
  • get-markdown-from-media: Uses AI magic (requires a “magic key” from OpenAI) to extract text from PDFs and images.

Use Cases of mcp-server-fetch-python: Streamlined Server Fetch & DevOps Automation?

Ever tried scraping a news site that’s “anti-bot”? This server is your invisibility cloak. Use cases include:

  • Automating inventory checks for e-commerce price wars
  • Building news aggregators that don’t choke on ad-heavy pages
  • Extracting specs from manufacturer PDFs for product databases
  • Monitoring social media dashboards without manually refreshing

mcp-server-fetch-python FAQ

FAQ from mcp-server-fetch-python: Streamlined Server Fetch & DevOps Automation?

Q: What’s with the “magic key” requirement?
A: The AI-powered media parser needs OpenAI’s API key—a tiny price to pay for beating CAPTCHAs.

Q: Can I use this on my corporate server?
A: Absolutely! It’s MIT-licensed, but check your company’s firewall policies before summoning web content demons.

Q: What if my data has weird characters?
A: Use UTF-8 encoding and the iconv module—it’s like a character encoding SWAT team.

Content

mcp-server-fetch-python

An MCP server for fetching and transforming web content into various formats. This server provides comprehensive tools for extracting content from web pages, including support for JavaScript-rendered content and media files.

Server Fetch Python MCP server

Features

Tools

The server provides four specialized tools:

  • get-raw-text : Extracts raw text content directly from URLs without browser rendering

    • Arguments:
      • url: URL of the target web page (text, JSON, XML, csv, tsv, etc.) (required)
    • Best used for structured data formats or when fast, direct access is needed
  • get-rendered-html : Fetches fully rendered HTML content using a headless browser

    • Arguments:
      • url: URL of the target web page (required)
    • Essential for modern web applications and SPAs that require JavaScript rendering
  • get-markdown : Converts web page content to well-formatted Markdown

    • Arguments:
      • url: URL of the target web page (required)
    • Preserves structural elements while providing clean, readable text output
  • get-markdown-from-media : Performs AI-powered content extraction from media files

    • Arguments:
      • url: URL of the target media file (images, videos) (required)
    • Utilizes computer vision and OCR for visual content analysis
    • Requires a valid OPENAI_API_KEY to be set in environment variables
    • Will return an error message if the API key is not set or if there are issues processing the media file

Usage

Claude Desktop

To use with Claude Desktop, add the server configuration:

On MacOS: ~/Library/Application\ Support/Claude/claude_desktop_config.json
On Windows: %APPDATA%/Claude/claude_desktop_config.json

"mcpServers": {
  "mcp-server-fetch-python": {
    "command": "uvx",
    "args": [
      "mcp-server-fetch-python"
    ]
  }
}

Environment Variables

The following environment variables can be configured:

  • OPENAI_API_KEY : Required for using the get-markdown-from-media tool. This key is needed for AI-powered image analysis and content extraction.

  • PYTHONIOENCODING : Set to "utf-8" if you encounter character encoding issues in the output.

  • MODEL_NAME : Specifies the model name to use. Defaults to "gpt-4o".

    "mcpServers": {
    "mcp-server-fetch-python": {
    "command": "uvx",
    "args": [
    "mcp-server-fetch-python"
    ],
    "env": {
    "OPENAI_API_KEY": "sk-****",
    "PYTHONIOENCODING": "utf-8",
    "MODEL_NAME": "gpt-4o",
    }
    }
    }

Local Installation

Alternatively, you can install and run the server locally:

git clone https://github.com/tatn/mcp-server-fetch-python.git
cd mcp-server-fetch-python
uv sync
uv build

Then add the following configuration to Claude Desktop config file:

"mcpServers": {
  "mcp-server-fetch-python": {
    "command": "uv",
    "args": [
      "--directory",
      "path\\to\\mcp-server-fetch-python",  # Replace with actual path to the cloned repository
      "run",
      "mcp-server-fetch-python"
    ]
  }
}

Development

Debugging

You can start the MCP Inspector using npxwith the following commands:

npx @modelcontextprotocol/inspector uvx mcp-server-fetch-python



npx @modelcontextprotocol/inspector uv --directory path\\to\\mcp-server-fetch-python run mcp-server-fetch-python

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