Web Content Extractor MCP Server
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
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| extract_articleA | Extract clean article content from any URL as agent-optimized markdown. Uses advanced content extraction to get main article text, metadata, and reading stats. Perfect for agents processing news, blogs, documentation. |
| extract_structured_dataA | Extract structured data (tables, lists, key-value pairs) from any webpage as JSON. Perfect for agents that need to process data tables, pricing lists, feature comparisons, or any structured web content. |
| extract_linksA | Get all links from a webpage with intelligent categorization and context. Returns internal/external links, link text, and destination context. Essential for agents doing competitive analysis, site mapping, or link discovery. |
| screenshot_to_markdownB | Take a screenshot of a webpage and convert visual layout to structured markdown description. Perfect for agents that need to understand page layout, UI elements, or visual content when text extraction is insufficient. |
| batch_extractA | Process multiple URLs in parallel and return consolidated results. Highly efficient for agents that need to analyze multiple pages, compare content, or do batch research. Includes rate limiting and error recovery. |
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 5 tools
Each tool targets a distinct aspect of web content extraction: article text, structured data, links, visual layout, and batch processing. No overlapping purposes.
All tool names follow a consistent verb_noun pattern using snake_case (e.g., extract_article, screenshot_to_markdown). The naming is predictable and clear.
5 tools is an ideal number for this server's scope, covering all major content extraction needs without being excessive or too sparse.
The set covers article extraction, structured data, links, visual rendering, and batch processing. A minor gap is the lack of a unified 'extract all' tool, but the current surface is comprehensive for most use cases.