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StripFeed

stripfeed-mcp-server

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batch_fetch

Fetch multiple URLs simultaneously and convert them to clean Markdown format. Processes up to 10 URLs in parallel, removing ads and unnecessary elements for AI processing.

Instructions

Fetch multiple URLs in parallel and convert them all to clean Markdown. Process up to 10 URLs in a single call.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlsYesArray of URLs to fetch (1-10)
modelNoAI model ID for cost tracking

Implementation Reference

  • The full handler implementation for the "batch_fetch" MCP tool, including registration, input validation using Zod, API interaction, and response formatting.
    server.tool(
      "batch_fetch",
      "Fetch multiple URLs in parallel and convert them all to clean Markdown. Process up to 10 URLs in a single call.",
      {
        urls: z
          .array(z.string().url())
          .min(1)
          .max(10)
          .describe("Array of URLs to fetch (1-10)"),
        model: z
          .string()
          .optional()
          .describe("AI model ID for cost tracking"),
      },
      async (params) => {
        const apiKey = getApiKey();
    
        const response = await fetch(`${BASE_URL}/batch`, {
          method: "POST",
          headers: {
            Authorization: `Bearer ${apiKey}`,
            "Content-Type": "application/json",
          },
          body: JSON.stringify({
            urls: params.urls,
            model: params.model,
          }),
        });
    
        if (!response.ok) {
          const body = await response.text();
          let message: string;
          try {
            message = JSON.parse(body).error;
          } catch {
            message = body;
          }
          throw new Error(`StripFeed API error ${response.status}: ${message}`);
        }
    
        const data = await response.json();
        const results = data.results as Array<{
          url: string;
          title: string;
          markdown: string;
          tokens: number;
          originalTokens: number;
          savingsPercent: number;
          status: number;
          error?: string;
        }>;
    
        const sections = results.map((r) => {
          if (r.status !== 200) {
            return `## ${r.url}\n\nError: ${r.error ?? `Status ${r.status}`}`;
          }
          const saved = `${r.tokens.toLocaleString()} tokens (saved ${r.savingsPercent}% from ${r.originalTokens.toLocaleString()})`;
          return `## ${r.title || r.url}\n\nSource: ${r.url} | ${saved}\n\n${r.markdown}`;
        });
    
        const summary = `Fetched ${data.success}/${data.total} URLs successfully.`;
    
        return {
          content: [
            { type: "text" as const, text: `${summary}\n\n---\n\n${sections.join("\n\n---\n\n")}` },
          ],
        };
      }
    );
Behavior4/5

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

With no annotations, description carries full burden. Discloses parallel execution, Markdown conversion as output format, and the 10 URL constraint. Missing error handling details (e.g., partial failure behavior) or authentication requirements.

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?

Two sentences with zero waste. Core action front-loaded in first sentence; constraint clearly stated in second. Every word earns its place.

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?

Appropriately complete for a 2-parameter tool without annotations. Covers the key operational constraints (10 URL limit, parallel processing, Markdown output). No output schema exists, but output format is described in the description.

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

Parameters4/5

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

Schema has 100% coverage (baseline 3). Description adds value by reinforcing the 10 URL limit and specifying 'parallel' processing mode for the urls parameter, adding behavioral context not present in the schema.

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?

Clear specific verb (Fetch/convert), resource (URLs), and output format (clean Markdown). 'Multiple URLs in parallel' effectively distinguishes from sibling fetch_url which presumably handles single URLs.

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?

Provides clear context that this tool is for batch operations ('multiple URLs', 'up to 10 URLs in a single call'), implying when to use it over fetch_url. Lacks explicit 'use fetch_url for single URLs' guidance, preventing a 5.

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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