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audio-to-markdown

Convert audio files to Markdown format with transcription. Transforms spoken content into structured text for documentation, notes, or content creation.

Instructions

Convert an audio file to markdown, including transcription if possible

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
filepathYesAbsolute path of the audio file to convert

Implementation Reference

  • Switch case that handles the call to audio-to-markdown tool (shared with other file-to-markdown tools), validates input filepath and delegates to Markdownify.toMarkdown.
    case tools.PDFToMarkdownTool.name:
    case tools.ImageToMarkdownTool.name:
    case tools.AudioToMarkdownTool.name:
    case tools.DocxToMarkdownTool.name:
    case tools.XlsxToMarkdownTool.name:
    case tools.PptxToMarkdownTool.name:
      if (!validatedArgs.filepath) {
        throw new Error("File path is required for this tool");
      }
      result = await Markdownify.toMarkdown({
        filePath: validatedArgs.filepath,
        projectRoot: validatedArgs.projectRoot,
        uvPath: validatedArgs.uvPath || process.env.UV_PATH,
      });
      break;
  • Core handler function that performs the file-to-markdown conversion for audio files (and others) by invoking the markitdown CLI tool.
    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);
    
          let extension = null;
    
          if (url.endsWith(".pdf")) {
            extension = "pdf";
          }
    
          const arrayBuffer = await response.arrayBuffer();
          const content = Buffer.from(arrayBuffer);
    
          inputPath = await this.saveToTempFile(content, extension);
          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: ${e.message}`);
        } else {
          throw new Error("Error processing to Markdown: Unknown error occurred");
        }
      }
    }
  • Input schema definition for the audio-to-markdown tool, specifying the required filepath parameter.
    export const AudioToMarkdownTool = ToolSchema.parse({
      name: "audio-to-markdown",
      description:
        "Convert an audio file to markdown, including transcription if possible",
      inputSchema: {
        type: "object",
        properties: {
          filepath: {
            type: "string",
            description: "Absolute path of the audio file to convert",
          },
        },
        required: ["filepath"],
      },
    });
  • src/server.ts:33-37 (registration)
    Registers the audio-to-markdown tool (along with others from tools.ts) for the MCP listTools request.
    server.setRequestHandler(ListToolsRequestSchema, async () => {
      return {
        tools: Object.values(tools),
      };
    });
  • Helper function that executes the markitdown CLI tool via uv to convert the audio file to markdown text.
    private static async _markitdown(
      filePath: string,
      projectRoot: string,
      uvPath: string,
    ): Promise<string> {
      const venvPath = path.join(projectRoot, ".venv");
      const markitdownPath = path.join(
        venvPath,
        process.platform === "win32" ? "Scripts" : "bin",
        `markitdown${process.platform === "win32" ? ".exe" : ""}`,
      );
    
      if (!fs.existsSync(markitdownPath)) {
        throw new Error("markitdown executable not found");
      }
    
      // Expand tilde in uvPath if present
      const expandedUvPath = expandHome(uvPath);
    
      // Use execFile to prevent command injection
      const { stdout, stderr } = await execFileAsync(expandedUvPath, [
        "run",
        markitdownPath,
        filePath,
      ]);
    
      if (stderr) {
        throw new Error(`Error executing command: ${stderr}`);
      }
    
      return stdout;
    }
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'including transcription if possible', which hints at potential limitations, but doesn't specify what happens when transcription fails, required audio formats, processing time, or output structure. For a tool with no annotations, this leaves significant behavioral gaps.

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 that directly states the tool's purpose. It's front-loaded with the core functionality and includes a useful qualification ('if possible'). Every word earns its place with zero waste.

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 the tool's moderate complexity (audio processing and transcription), no annotations, no output schema, and 100% schema coverage, the description is minimally adequate. It covers the basic purpose but lacks details about behavioral constraints, output format, or error conditions that would be helpful for an AI agent.

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 has 100% description coverage, with the 'filepath' parameter clearly documented. The description adds no additional parameter information beyond what the schema provides. According to the rules, when schema coverage is high (>80%), the baseline score is 3 even with no param info in the description.

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 tool's purpose: 'Convert an audio file to markdown, including transcription if possible'. This specifies the verb ('convert'), resource ('audio file'), and output format ('markdown'), distinguishing it from siblings that convert other file types. However, it doesn't explicitly differentiate from all siblings (e.g., 'youtube-to-markdown' also involves audio conversion).

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 alternatives. It doesn't mention sibling tools, prerequisites, or specific contexts where audio-to-markdown is preferred over other conversion tools. The phrase 'if possible' hints at limitations but doesn't specify conditions for successful transcription.

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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