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Clawy MCP Server

by ClawyPro

firecrawl_scrape

Extract clean, structured content from any URL with JavaScript rendering and bypass common blocks. Returns markdown optimized for LLM processing.

Instructions

Scrape a single URL and extract clean, structured content. Handles JavaScript rendering and bypasses common blocks. Returns markdown optimized for LLMs.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesURL to scrape
formatsNoOutput formats (default: ['markdown'])
onlyMainContentNoExtract only main content, skip nav/footer (default true)
waitForNoWait milliseconds for JS rendering

Implementation Reference

  • Definition and registration of the 'firecrawl_scrape' tool.
    {
      name: "firecrawl_scrape",
      description: "Scrape a single URL and extract clean, structured content. Handles JavaScript rendering and bypasses common blocks. Returns markdown optimized for LLMs.",
      inputSchema: z.object({
        url: z.string().describe("URL to scrape"),
        formats: z.array(z.enum(["markdown", "html", "rawHtml", "links", "screenshot"])).optional()
          .describe("Output formats (default: ['markdown'])"),
        onlyMainContent: z.boolean().optional().describe("Extract only main content, skip nav/footer (default true)"),
        waitFor: z.number().optional().describe("Wait milliseconds for JS rendering"),
      }),
      endpoint: "/v1/firecrawl/scrape",
    },
  • The generic handler for all registered tools, including firecrawl_scrape. It uses gatewayRequest to fetch data from the API endpoint defined in tool configurations.
    // Register all tools
    for (const tool of allTools) {
      server.tool(
        tool.name,
        tool.description,
        tool.inputSchema.shape,
        async (params) => {
          const method = tool.method || "POST";
          const result = await gatewayRequest(method, tool.endpoint, params as Record<string, unknown>);
    
          if (result.error) {
            return {
              content: [{ type: "text" as const, text: `Error (${result.status}): ${result.error}` }],
              isError: true,
            };
          }
    
          const text = typeof result.data === "string"
            ? result.data
            : JSON.stringify(result.data, null, 2);
    
          return {
            content: [{ type: "text" as const, text }],
          };
        },
      );
Behavior3/5

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

No annotations are provided, so the description carries full burden. It discloses key behavioral traits: JavaScript rendering, bypassing common blocks, and markdown optimization for LLMs. However, it lacks details on rate limits, error handling, authentication needs, or what 'clean, structured content' entails beyond markdown.

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

Conciseness5/5

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

The description is front-loaded and concise with two sentences that efficiently convey core functionality and key features. Every sentence adds value without redundancy, making it easy to parse quickly.

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

Completeness3/5

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 is moderately complete for a scraping tool. It covers purpose and some behaviors but lacks details on output structure, error cases, or performance constraints, which could be important for an agent invoking this tool effectively.

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%, providing baseline documentation for all parameters. The description adds minimal semantics by mentioning 'clean, structured content' and 'markdown optimized for LLMs', which loosely relates to the 'formats' parameter but does not elaborate on parameter interactions or default behaviors beyond what the schema states.

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 ('scrape', 'extract') and resource ('a single URL'), and distinguishes it from sibling tools like firecrawl_crawl and firecrawl_map by specifying 'single URL' rather than crawling or mapping multiple URLs.

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

Usage Guidelines3/5

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

The description implies usage context ('extract clean, structured content', 'handles JavaScript rendering') but does not explicitly state when to use this tool versus alternatives like firecrawl_crawl for multiple URLs or other scraping tools. No exclusions or prerequisites are mentioned.

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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