Qwen Video Understanding MCP Server

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

No annotations are provided, so the description carries the full burden. It describes what the tool does (extract and transcribe text/speech) and gives usage examples, but lacks details on behavioral traits like processing time, accuracy, supported video formats, or error handling. This leaves gaps in understanding operational constraints.

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

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

The description is well-structured and front-loaded, starting with a clear purpose statement followed by a bulleted list of use cases. Every sentence earns its place by providing specific, actionable information without redundancy or fluff.

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

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

Given no annotations and no output schema, the description is incomplete for a tool that performs complex video processing. It explains the purpose and usage but lacks details on output format (e.g., text structure, timestamps), limitations (e.g., video length, language support), or error conditions, which are important for contextual understanding.

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

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

The input schema has 100% description coverage, with the single parameter 'video_url' clearly documented. The description does not add extra parameter details beyond the schema, but since schema coverage is high and there are no parameters beyond this, a baseline of 4 is appropriate as it adequately covers the minimal parameter set.

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

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

The description explicitly states the verb 'extract and transcribe' and the resource 'visible text or speech from a video', making the purpose specific and clear. It distinguishes from siblings like 'summarize_video' or 'analyze_video' by focusing specifically on text extraction rather than analysis or summarization.

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

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

The 'Useful for' section provides clear context on when to use this tool, listing specific scenarios like reading on-screen text, transcribing speech, and extracting text from presentations. However, it does not explicitly state when not to use it or name alternatives among siblings, such as 'summarize_video' for summaries instead of full text extraction.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.