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Fetch MCP Server

by goswamig

fetch_markdown

Convert website content into Markdown format by specifying the URL and optional headers for web requests. Facilitates structured extraction for documentation or processing needs.

Instructions

Fetch a website and return the content as Markdown

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
headersNoOptional headers to include in the request
urlYesURL of the website to fetch

Implementation Reference

  • The primary handler function executing the fetch_markdown tool: fetches HTML from the URL and converts it to Markdown using TurndownService.
    static async markdown(requestPayload: RequestPayload) {
      try {
        const response = await this._fetch(requestPayload);
        const html = await response.text();
        const turndownService = new TurndownService();
        const markdown = turndownService.turndown(html);
        return { content: [{ type: "text", text: markdown }], isError: false };
      } catch (error) {
        return {
          content: [{ type: "text", text: (error as Error).message }],
          isError: true,
        };
      }
    }
  • Zod schema defining the input structure for fetch_markdown (and other fetch tools): requires a valid URL and optional headers.
    export const RequestPayloadSchema = z.object({
      url: z.string().url(),
      headers: z.record(z.string()).optional(),
    });
  • src/index.ts:46-63 (registration)
    Tool registration in the ListTools response, providing name, description, and input schema.
    {
      name: "fetch_markdown",
      description: "Fetch a website and return the content as Markdown",
      inputSchema: {
        type: "object",
        properties: {
          url: {
            type: "string",
            description: "URL of the website to fetch",
          },
          headers: {
            type: "object",
            description: "Optional headers to include in the request",
          },
        },
        required: ["url"],
      },
    },
  • Dispatch logic in the CallToolRequestHandler that routes fetch_markdown calls to the Fetcher.markdown implementation.
    if (request.params.name === "fetch_markdown") {
      const fetchResult = await Fetcher.markdown(validatedArgs);
      return fetchResult;
    }
  • Shared private helper method for performing the HTTP fetch request with custom User-Agent and error handling, used by markdown handler.
    private static async _fetch({
      url,
      headers,
    }: RequestPayload): Promise<Response> {
      try {
        const response = await fetch(url, {
          headers: {
            "User-Agent":
              "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36",
            ...headers,
          },
        });
    
        if (!response.ok) {
          throw new Error(`HTTP error: ${response.status}`);
        }
        return response;
      } catch (e: unknown) {
        if (e instanceof Error) {
          throw new Error(`Failed to fetch ${url}: ${e.message}`);
        } else {
          throw new Error(`Failed to fetch ${url}: Unknown error`);
        }
      }
    }
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool fetches and converts to Markdown but lacks details on error handling, rate limits, authentication needs, or whether it performs web scraping vs. API calls. This is inadequate for a tool that interacts with external websites.

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 a single, efficient sentence with zero wasted words. It front-loads the core purpose and output, making it easy to parse quickly.

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

Completeness2/5

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

For a tool that fetches external websites with 2 parameters and no annotations or output schema, the description is incomplete. It doesn't cover behavioral aspects like error cases, conversion limitations, or output structure, leaving significant gaps for an AI agent to use it correctly.

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 schema fully documents both parameters (url and headers). The description adds no additional meaning beyond implying the URL is for a website, which is already clear from the schema. Baseline 3 is appropriate as the schema does the heavy lifting.

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

Purpose4/5

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

The description clearly states the action ('Fetch a website') and the output format ('return the content as Markdown'), which distinguishes it from sibling tools like fetch_html, fetch_json, and fetch_txt that return different formats. However, it doesn't explicitly mention how it differs beyond output format (e.g., conversion process).

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 provides no guidance on when to use this tool versus its siblings. It doesn't mention scenarios where Markdown output is preferred over HTML, JSON, or plain text, nor does it specify prerequisites like URL accessibility or content type suitability.

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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