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 adds valuable context about the image accessibility requirement (public URL) and provides example use cases that suggest the tool's capabilities. However, it doesn't disclose important behavioral traits like rate limits, authentication needs, error conditions, or response format expectations that would be crucial for an agent to use it effectively.

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 perfectly structured and concise. It leads with the core purpose, follows with the critical accessibility constraint, then provides helpful examples that demonstrate the tool's capabilities without being verbose. Every sentence earns its place, and the bulleted examples are efficiently formatted to convey multiple use cases in minimal space.

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 provides adequate but incomplete context. It covers the purpose, accessibility requirement, and example use cases well, but lacks information about what the tool returns, error conditions, rate limits, or authentication requirements. For a tool with 3 parameters and no structured behavioral annotations, this leaves significant gaps in understanding how to properly invoke and interpret results from this tool.

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 fully documents all three parameters. The description doesn't add any parameter-specific information beyond what's in the schema. It provides example questions that illustrate potential values for the 'question' parameter, but this doesn't add semantic meaning beyond the schema's description. Baseline 3 is appropriate when the schema does all the parameter documentation work.

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: 'Analyze an image using Qwen2.5-VL vision-language model.' It specifies the exact model being used and distinguishes it from sibling tools like analyze_video, video_qa, and compare_video_frames by focusing specifically on image analysis rather than video processing.

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 about when to use this tool: for analyzing images via a specific vision-language model. It explicitly states 'The image must be accessible via a public URL,' establishing a key prerequisite. However, it doesn't explicitly contrast when NOT to use it versus alternatives like analyze_video or video_qa, though the image focus is implied.

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