analyze_data_visualization

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 clearly discloses that the tool reads dashboards/charts and extracts trends, anomalies, and business points, which is the core behavior. However, it does not mention the output format or any limitations (e.g., single image, chart clarity), so it is not fully transparent.

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 very concise: a single-sentence purpose statement followed by a bulleted list of use cases and exclusions. Every sentence earns its place, and the structure front-loads the main purpose before giving usage guidance.

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 that there is no output schema, the description does not explain the exact return format, but it does state what the tool produces (trends, anomalies, business points). It covers when to use, when not to use, and the core functionality, making it fairly complete for a single-image analysis 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?

All three parameters have schema descriptions (100% coverage), so the baseline is 3. The description adds value by aligning its four use cases (extract insights, identify trends, detect anomalies, business impact) with the `analysis_focus` parameter's possible values, and it implicitly instructs how to phrase the `prompt` parameter.

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 uses specific verbs (阅读/提炼) and identifies the resource (仪表盘、统计图表), clearly stating it analyzes data visualizations to extract trends, anomalies, and business insights. It also explicitly differentiates from siblings by stating what it is NOT for: technical architecture diagrams, UI screenshots, and error diagnosis.

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 explicitly says '仅在用户需要以下操作时使用' (only use when the user needs the following operations) and lists four concrete use cases. It also provides a '不要用于' (do not use for) list with three exclusions, which clearly guides the agent away from using it on non-data-visualization inputs.

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