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scrape

Convert URLs to clean Markdown for AI processing, handling JavaScript-heavy sites, Cloudflare protection, and dynamic content with built-in CAPTCHA solving.

Instructions

Convert any URL to clean, LLM-ready Markdown. 84% success rate including JavaScript-heavy sites, Cloudflare-protected pages, and government sites. Renders JavaScript, handles dynamic content, bypasses common bot detection with stealth mode and CAPTCHA solving. Returns structured markdown with title and metadata. Tip: provide 'context' to get more relevant results. Free tier: 10 scrapes per day. Get 50 per day at anybrowse.dev/upgrade-free

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesThe URL to scrape (must start with http:// or https://)
contextNoOptional: what you're trying to accomplish (e.g., 'comparing job salaries', 'researching competitors', 'extracting product prices'). Helps anybrowse return more relevant content.

Implementation Reference

  • The "scrape" tool is registered in the MCP server using the `server.tool` method. It accepts a `url` and an optional `format`, then calls the `/scrape` API endpoint.
    server.tool(
      "scrape",
      "Convert a URL to clean Markdown. Extracts the main content from any webpage.",
      {
        url: z.string().url().describe("The URL to scrape"),
        format: z
          .enum(["markdown", "text", "html"])
          .optional()
          .describe("Output format (default: markdown)"),
      },
      async ({ url, format }) => {
        const data = await callApi("/scrape", { url, format });
        return {
          content: [{ type: "text" as const, text: data.content || "" }],
        };
      }
    );
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden and does an excellent job disclosing behavioral traits: success rate (84%), capabilities (handles JavaScript-heavy sites, Cloudflare protection, dynamic content), features (stealth mode, CAPTCHA solving), return format (structured markdown with title/metadata), and usage limits (free tier: 10 scrapes per day). It doesn't mention error handling or rate limits beyond the daily cap.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is appropriately sized but not optimally structured. It front-loads the core purpose but mixes promotional content ('Get 50 per day at anybrowse.dev/upgrade-free') with functional information. Some sentences like the upgrade prompt don't directly help the AI agent understand tool usage.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no annotations and no output schema, the description provides substantial context about capabilities, limitations, and behavior. It covers success rates, technical capabilities, return format, and usage limits. However, it doesn't describe error conditions, response formats in detail, or how the 'context' parameter quantitatively affects results.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the baseline is 3. The description adds minimal parameter semantics beyond the schema - it mentions the 'context' parameter tip but doesn't elaborate on how it affects results. The description focuses more on behavioral aspects than parameter details.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose with specific verbs ('Convert', 'Returns') and resource ('any URL to clean, LLM-ready Markdown'). It distinguishes from siblings by specifying unique capabilities like JavaScript rendering and bot detection bypassing that aren't implied by generic names like 'batch_scrape' or 'extract'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context for when to use this tool ('Convert any URL to clean, LLM-ready Markdown') and mentions a tip for better results ('provide context to get more relevant results'), but doesn't explicitly state when NOT to use it or compare it to alternatives like 'batch_scrape' or 'crawl' that might be more appropriate for different scenarios.

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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