MCP Server Fetch Python
# 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.
<a href="https://glama.ai/mcp/servers/8d0zm2o56d"><img width="380" height="200" src="https://glama.ai/mcp/servers/8d0zm2o56d/badge" alt="Server Fetch Python MCP server" /></a>
## 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`
```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".
```json
"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:
```powershell
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:
```json
"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 [npx](https://docs.npmjs.com/cli/v11/commands/npx)with the following commands:
```bash
npx @modelcontextprotocol/inspector uvx mcp-server-fetch-python
```
```bash
npx @modelcontextprotocol/inspector uv --directory path\\to\\mcp-server-fetch-python run mcp-server-fetch-python
```
TDQS
Scored across 4 tools
Each tool has a clearly distinct purpose with no overlap: get-markdown for general web content conversion, get-markdown-from-media for AI-powered media extraction, get-raw-text for direct access to structured data, and get-rendered-html for JavaScript-rendered content. The descriptions explicitly differentiate use cases, preventing misselection.
All tool names follow a consistent verb_noun pattern with 'get-' prefix and descriptive suffixes (e.g., get-markdown, get-raw-text). This uniformity makes the tool set predictable and easy to navigate, with no deviations in naming style.
With 4 tools, the server is well-scoped for web content extraction, covering key scenarios like general conversion, media analysis, raw data access, and rendered content. Each tool earns its place without redundancy, and the count is appropriate for the domain.
The tool set provides complete coverage for web content extraction, addressing diverse needs from structured data to dynamic pages and media files. There are no obvious gaps; agents can handle various extraction workflows without dead ends.