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 of behavioral disclosure. It states the tool answers questions about video content but does not explain how it works (e.g., AI-based analysis, processing time, limitations like video length or format support), what happens if the question is unclear, or the response format. For a tool with no annotations, this leaves significant gaps in understanding its behavior and 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 front-loaded with a clear purpose statement, followed by relevant examples that earn their place by illustrating usage without redundancy. It is appropriately sized—two sentences and a bulleted list—with zero waste, making it easy to scan and understand quickly.

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 the tool's moderate complexity (Q&A based on video analysis), no annotations, and no output schema, the description is incomplete. It covers the purpose and parameter intent but lacks details on behavioral traits, response format, and limitations. While the examples add context, more information is needed for a tool that performs AI-driven video analysis, making it only adequate with clear gaps.

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?

Schema description coverage is 100%, so the schema already documents both parameters ('video_url' and 'question'). The description adds value by emphasizing that questions should be 'specific' and providing examples that illustrate the expected format and scope of the 'question' parameter, though it doesn't detail URL requirements. This goes beyond the schema's basic descriptions, justifying a score above the baseline of 3.

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 clearly states the tool's purpose: 'Ask a specific question about a video's content.' It specifies the verb ('Ask'), resource ('video's content'), and provides concrete examples that illustrate the scope. This distinguishes it from siblings like 'summarize_video' or 'analyze_video' by focusing on Q&A rather than general 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 description implies usage through examples (e.g., questions about objects, actions, arguments), suggesting it's for specific queries rather than broad analysis. However, it lacks explicit guidance on when to use this tool versus alternatives like 'analyze_video' or 'summarize_video', and does not mention prerequisites or exclusions. The examples help but don't fully clarify the tool's niche among siblings.

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