Scrape any public URL and convert web pages into clean, token-efficient LLM Markdown for AI workflows, priced at $0.005 USDC on Base.
USE WHEN: Scraping any public URL and converting the page into clean, token-efficient LLM Markdown ($0.005 USDC on Base).
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | Target HTTP/HTTPS URL to scrape |
Changes observed during successful MCP inspections.
v1.0.1Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full disclosure burden. It does add real behavioral context beyond the schema: a $0.005 USDC cost on Base (a payment gate the agent must account for) and the output format (LLM Markdown). It omits rate limits, JS-rendering behavior, failure modes, and size limits, which matters for a tool with zero annotation coverage.
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?
A single front-loaded sentence with the trigger condition first and the cost/output details trailing. No filler.
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?
For a one-parameter, no-output-schema tool the description covers purpose, trigger, output format, and cost. Only operational details like error behavior and page-size limits are absent, which are minor for this simplicity level.
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% for the single url parameter, so the baseline is already high. The description adds a constraint beyond the schema by specifying 'public URL', narrowing acceptable inputs.
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?
States a specific verb (scraping) and resource (public URL) plus the transformation (page → clean LLM Markdown). The conversion output is a meaningful differentiator against the audit-oriented siblings, though it never names them directly.
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?
The 'USE WHEN:' prefix gives a clear triggering condition: any public URL to be scraped. There are no explicit when-not conditions or named alternatives, but for a single-purpose scrape tool the trigger is unambiguous.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.