MCP Server Fetch Python
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Server capabilities have not been inspected yet.
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| get-raw-textA | Extracts raw text content directly from URLs without browser rendering. Ideal for structured data formats like JSON, XML, CSV, TSV, or plain text files. Best used when fast, direct access to the source content is needed without processing dynamic elements. |
| get-rendered-htmlB | Fetches fully rendered HTML content using a headless browser, including JavaScript-generated content. Essential for modern web applications, single-page applications (SPAs), or any content that requires client-side rendering to be complete. |
| get-markdownA | Converts web page content to well-formatted Markdown, preserving structural elements like tables and definition lists. Recommended as the default tool for web content extraction when a clean, readable text format is needed while maintaining document structure. |
| get-markdown-from-mediaB | Performs AI-powered content extraction from media files (images and videos) and converts the results to Markdown format. Specialized tool for visual content analysis that utilizes computer vision and OCR capabilities to generate descriptive text from media sources. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
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.