Web tools for agents: fetch URL as markdown (free MCP) + x402 scrape, links, AI JSON, snapshot.
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Average 4.2/5 across 1 of 1 tools scored.
With only one tool, there is no possibility for confusion between tools. The tool 'extract' has a clear and distinct purpose.
The single tool name 'extract' follows a verb pattern, which is consistent and appropriate for its action.
One tool for web scraping is somewhat thin, as a typical scraping workflow might involve multiple steps. However, the tool is comprehensive and covers the core use case, making it borderline appropriate.
The tool covers the primary need of fetching web pages and converting to Markdown, including JS rendering. Minor gaps like authentication or element-specific extraction exist but are workable.
extractAInspectFetch any public web page and return its main content as clean, token-efficient Markdown (title, description, headings, links, lists). Set render:true to execute JavaScript first for single-page apps or JS-heavy pages that would otherwise come back empty. Built for RAG and for agents that need to read the contents of a URL.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | Full http/https URL of the page to extract. | |
| render | No | Render JavaScript with a headless browser before extracting (default false). Enable for SPAs / JS-heavy pages that return empty content otherwise. Slower. | |
| max_chars | No | Truncate the markdown to at most this many characters (sets truncated:true). | |
| include_links | No | Keep hyperlinks in the markdown output (default true). | |
| include_images | No | Keep images as markdown (default false). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. Mentions Markdown output and render behavior but lacks details on error handling, rate limits, or access restrictions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with core purpose, second sentence explains key parameter. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Comprehensive for a web extraction tool with no siblings and no output schema. Covers main behavior and key parameter, though error handling and size limits are omitted.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, baseline 3. Description adds meaning by explaining render's purpose with performance trade-off and that max_chars sets truncated:true, exceeding schema detail.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the tool fetches any public web page and returns main content as Markdown, listing included elements. Verb+resource is specific and unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides guidance on when to use render:true (SPAs/JS-heavy pages) and that it's slower. No sibling tools to differentiate, but context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
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