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JDJR2024

Markdownify MCP Server - UTF-8 Enhanced

by JDJR2024

youtube-to-markdown

Convert YouTube videos to markdown format with included transcripts using the Markdownify MCP Server, supporting enhanced UTF-8 and multilingual content.

Instructions

Convert a YouTube video to markdown, including transcript if available

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesURL of the YouTube video

Implementation Reference

  • Core handler function that fetches content from the provided YouTube URL, saves it to a temporary file, processes it using the markitdown tool to generate markdown, and returns the markdown text along with its file path.
    static async toMarkdown({
      filePath,
      url,
      projectRoot = path.resolve(__dirname, ".."),
      uvPath = "~/.local/bin/uv",
    }: {
      filePath?: string;
      url?: string;
      projectRoot?: string;
      uvPath?: string;
    }): Promise<MarkdownResult> {
      try {
        let inputPath: string;
        let isTemporary = false;
    
        if (url) {
          const response = await fetch(url);
          const content = await response.text();
          inputPath = await this.saveToTempFile(content);
          isTemporary = true;
        } else if (filePath) {
          inputPath = filePath;
        } else {
          throw new Error("Either filePath or url must be provided");
        }
    
        const text = await this._markitdown(inputPath, projectRoot, uvPath);
        const outputPath = await this.saveToTempFile(text);
    
        if (isTemporary) {
          fs.unlinkSync(inputPath);
        }
    
        return { path: outputPath, text };
      } catch (e: unknown) {
        if (e instanceof Error) {
          throw new Error(`Error processing to Markdown: ${e.message}`);
        } else {
          throw new Error("Error processing to Markdown: Unknown error occurred");
        }
      }
    }
  • Defines the tool schema including name, description, and input schema requiring a YouTube video URL.
    export const YouTubeToMarkdownTool = ToolSchema.parse({
      name: "youtube-to-markdown",
      description:
        "Convert a YouTube video to markdown, including transcript if available",
      inputSchema: {
        type: "object",
        properties: {
          url: {
            type: "string",
            description: "URL of the YouTube video",
          },
        },
        required: ["url"],
      },
    });
  • src/server.ts:47-58 (registration)
    Tool registration in the CallToolRequest handler switch statement, which validates the URL argument and delegates execution to the Markdownify.toMarkdown handler.
    case tools.YouTubeToMarkdownTool.name:
    case tools.BingSearchResultToMarkdownTool.name:
    case tools.WebpageToMarkdownTool.name:
      if (!validatedArgs.url) {
        throw new Error("URL is required for this tool");
      }
      result = await Markdownify.toMarkdown({
        url: validatedArgs.url,
        projectRoot: validatedArgs.projectRoot,
        uvPath: validatedArgs.uvPath || process.env.UV_PATH,
      });
      break;
Behavior2/5

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

With no annotations provided, the description carries full burden but offers minimal behavioral insight. It mentions transcript inclusion 'if available', hinting at conditional behavior, but lacks details on error handling, rate limits, authentication needs, or output format 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 a single, efficient sentence with zero waste. It's front-loaded with the core purpose and includes a useful qualifier about transcripts, making it appropriately concise.

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 with no annotations and no output schema, the description is incomplete. It doesn't explain what the markdown output contains (e.g., structure, metadata), potential errors, or dependencies like internet access, leaving significant gaps for agent usage.

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 already documents the 'url' parameter fully. The description adds no additional parameter semantics beyond implying the URL must be for a YouTube video, which is minimal value over the schema.

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 verb 'convert' and the resource 'YouTube video to markdown', specifying it includes transcript if available. It distinguishes from siblings by focusing on YouTube specifically, though it doesn't explicitly contrast with other conversion tools.

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?

No guidance is provided on when to use this tool versus alternatives like 'audio-to-markdown' or 'webpage-to-markdown'. The description implies usage for YouTube videos but doesn't mention prerequisites, limitations, or comparative advantages.

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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