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screenshot

Capture a rendered screenshot of any webpage. Specify URL and choose to capture full page.

Instructions

Capture a rendered screenshot of a webpage. Cost: 1 credit.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesURL to capture
full_pageNoCapture full scrollable page

Implementation Reference

  • The 'screenshot' capability definition with its input schema (url: string, full_page: optional boolean).
    {
      name: "screenshot",
      description: "Capture a rendered screenshot of a webpage. Cost: 1 credit.",
      inputSchema: {
        url: z.string().describe("URL to capture"),
        full_page: z.boolean().optional().default(false).describe("Capture full scrollable page"),
      },
    },
  • src/index.ts:247-259 (registration)
    All capabilities (including 'screenshot') are bulk-registered as MCP tools via the loop calling server.registerTool().
    for (const cap of CAPABILITIES) {
      // Cast inputSchema to avoid TS2589 (excessively deep type instantiation from Zod chains)
      server.registerTool(
        cap.name,
        {
          description: cap.description,
          inputSchema: cap.inputSchema as any,
        },
        async (args: any): Promise<CallToolResult> => {
          return callSuprsonic(cap.name, args as Record<string, unknown>);
        },
      );
    }
  • The generic handler function 'callSuprsonic' that all tools (including 'screenshot') use to call the Suprsonic REST API.
    async function callSuprsonic(capability: string, params: Record<string, unknown>): Promise<CallToolResult> {
      if (!API_KEY) {
        return {
          content: [{ type: "text", text: "Error: SUPRSONIC_API_KEY environment variable is not set. Get your key at https://suprsonic.ai/app/apis" }],
          isError: true,
        };
      }
    
      try {
        const resp = await fetch(`${BASE_URL}/v1/agent`, {
          method: "POST",
          headers: {
            "Authorization": `Bearer ${API_KEY}`,
            "Content-Type": "application/json",
          },
          body: JSON.stringify({ capability, params }),
        });
    
        const result = await resp.json() as any;
    
        // Handle non-envelope responses (401, 429, etc. return {"detail": ...})
        if (result.detail && result.success === undefined) {
          const msg = typeof result.detail === "object" ? (result.detail.title || result.detail.detail || JSON.stringify(result.detail)) : String(result.detail);
          return {
            content: [{ type: "text", text: `Error (HTTP ${resp.status}): ${msg}` }],
            isError: true,
          };
        }
    
        if (!result.success) {
          const errMsg = result.error?.detail || result.error?.title || "Request failed";
          return {
            content: [{ type: "text", text: `Error: ${errMsg}` }],
            isError: true,
          };
        }
    
        const text = JSON.stringify(result.data, null, 2);
        const meta = result.metadata
          ? `\n\n[Provider: ${(result.metadata as any).provider_used || "unknown"}, ${(result.metadata as any).response_time_ms || 0}ms, ${result.credits_used || 0} credits]`
          : "";
    
        return {
          content: [{ type: "text", text: text + meta }],
        };
      } catch (err) {
        return {
          content: [{ type: "text", text: `Network error: ${err instanceof Error ? err.message : String(err)}` }],
          isError: true,
        };
      }
    }
Behavior3/5

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

With no annotations, the description adds the cost (1 credit) and implies page rendering, but it lacks details on side effects (e.g., page load delays, potential failures on dynamic content, or resource consumption). The transparency is adequate but not thorough.

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 two short sentences, directly stating the core functionality and cost. There is no redundancy, and every word serves a purpose, making it highly efficient.

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?

For a simple screenshot tool with two parameters and no output schema, the description covers the essential purpose and cost. It could mention output format or error behavior, but it is sufficient for typical use.

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?

The input schema already has 100% coverage, describing 'url' and 'full_page' clearly. The description adds no additional meaning beyond the schema, so it meets the baseline without providing extra insight into parameter usage or constraints.

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 captures a rendered screenshot of a webpage, which is a specific verb+resource. This distinguishes it from sibling tools like 'scrape' (text extraction) or 'images' (image processing), making the purpose unambiguous.

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

Usage Guidelines2/5

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

The description only mentions cost but provides no guidance on when to use this tool versus alternatives like 'scrape' for text or 'images' for manipulation. There is no context about prerequisites or optimal use cases.

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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