Qwen Video Understanding MCP Server

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 effectively describes key behaviors: the video must be publicly accessible via URL, the process includes downloading, extracting frames (up to max_frames), analyzing with a question, and providing timestamp-grounded responses. It covers operational steps and constraints, though it lacks details on rate limits, error handling, or specific model capabilities.

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 the core purpose, followed by key requirements and a bulleted list of process steps. The examples are relevant and illustrative without redundancy. Every sentence earns its place, making it efficient and easy to scan.

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 complexity (video analysis with a vision-language model), no annotations, and no output schema, the description does a good job of covering the operational workflow, constraints, and use cases. It explains the process and provides examples, but lacks details on output format (e.g., structure of timestamp-grounded responses) and potential limitations, leaving some gaps for a tool of this nature.

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 all parameters (video_url, question, max_frames, max_tokens) with descriptions. The description adds minimal value beyond the schema, such as reinforcing the public URL requirement and hinting at frame extraction limits, but does not provide additional syntax or format details. Baseline 3 is appropriate as the schema does the heavy lifting.

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 tool's purpose: 'Analyze a video using Qwen3-VL vision-language model.' It specifies the action ('analyze'), resource ('video'), and method ('using Qwen3-VL vision-language model'), clearly distinguishing it from sibling tools like 'summarize_video' or 'extract_video_text' by emphasizing comprehensive analysis with question-driven responses.

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 provides clear context for when to use this tool: for analyzing videos with specific questions, as shown in the examples (e.g., 'What happens in this video?', 'Summarize the main events with timestamps'). It implies usage for detailed, timestamp-grounded analysis but does not explicitly state when not to use it or name alternatives among siblings, though the examples hint at its broad applicability.

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