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MCP Server Fetch Python

by tatn

get-markdown-from-media

Extract content from images and videos using AI, convert it into Markdown format for structured documentation. Ideal for transforming visual media into descriptive, text-based outputs.

Instructions

Performs AI-powered content extraction from media files (images and videos) and converts the results to Markdown format. Specialized tool for visual content analysis that utilizes computer vision and OCR capabilities to generate descriptive text from media sources.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesURL of the target web page (images, videos, etc.).

Implementation Reference

  • Executes the get-markdown-from-media tool by initializing an OpenAI client and using MarkItDown to convert the provided URL (media) to markdown text content.
    elif name == "get-markdown-from-media":
        if not config.OPENAI_API_KEY:
            raise ValueError("OPENAI_API_KEY is not set")
        client = OpenAI(api_key=config.OPENAI_API_KEY)
        md = MarkItDown(llm_client=client, llm_model=config.MODEL_NAME)
        result_string = md.convert(url).text_content
  • Registers the get-markdown-from-media tool with the MCP server, including its description and input schema requiring a 'url' parameter.
    types.Tool(
        name="get-markdown-from-media",
        description="Performs AI-powered content extraction from media files (images and videos) and converts the results to Markdown format. Specialized tool for visual content analysis that utilizes computer vision and OCR capabilities to generate descriptive text from media sources.",  # noqa: E501
        inputSchema={
            "type": "object",
            "properties": {
                "url": {"type": "string", "description":"URL of the target web page (images, videos, etc.)."}  # noqa: E501
            },
            "required": ["url"],
        },
    ), 
  • JSON schema for the get-markdown-from-media tool input, defining a required 'url' string parameter.
    inputSchema={
        "type": "object",
        "properties": {
            "url": {"type": "string", "description":"URL of the target web page (images, videos, etc.)."}  # noqa: E501
        },
        "required": ["url"],
    },
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 mentions AI-powered extraction and computer vision/OCR capabilities, which adds some context about the tool's approach. However, it does not disclose critical behavioral traits such as rate limits, authentication needs, error handling, or whether the operation is read-only or has side effects, which is a significant gap for a tool with no annotation coverage.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is appropriately sized with two sentences that are front-loaded with the core purpose. The first sentence clearly states the tool's function, and the second adds specialized context. There is minimal redundancy, though the second sentence could be slightly more concise.

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 complexity (AI-powered media analysis) and lack of annotations or output schema, the description is moderately complete. It explains the tool's purpose and specialization but omits details on output format, error conditions, and behavioral constraints. This leaves gaps for an agent to understand full usage, though the core functionality is adequately described.

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 'url' parameter documented as 'URL of the target web page (images, videos, etc.)'. The description does not add meaning beyond this, as it does not elaborate on parameter usage, format, or constraints. The baseline score of 3 is appropriate since the schema adequately covers the single parameter.

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's purpose: 'Performs AI-powered content extraction from media files (images and videos) and converts the results to Markdown format.' It specifies the action (extraction and conversion), resources (media files), and distinguishes from siblings by focusing on visual content analysis with computer vision/OCR rather than text or HTML processing.

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

Usage Guidelines3/5

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

The description implies usage context ('Specialized tool for visual content analysis') but does not explicitly state when to use this tool versus alternatives like 'get-markdown' or 'get-raw-text'. It suggests media files as input but lacks guidance on exclusions or prerequisites, leaving some ambiguity for the agent.

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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