Analyze Data Visualization

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

With no annotations, the description carries the full burden of behavioral disclosure, yet it only states the interpretative purpose. It does not disclose what the output looks like, what happens when 'question' is omitted, or how detail level affects results, leaving agents without key operational knowledge.

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 a single, front-loaded sentence that efficiently states the core purpose. It earns its place but is so brief that it leaves out important usage and transparency details, though it avoids any verbosity or redundancy.

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?

With no output schema and no annotations, the description should explain return values and default behavior, but it doesn't. It also fails to mention how the optional 'question' or 'detail' parameters influence interpretation, making it incomplete for a tool with five parameters.

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 coverage is 100%, with each parameter having a description, so the baseline is 3. The tool description adds no additional parameter semantics beyond what the schema already provides; it does not compensate for or enhance schema info.

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 the specific verb 'interpret' and clearly enumerates resource types (charts, graphs, tables, dashboards, metrics screenshots), distinguishing it from sibling tools like extract_text_from_image or diagnose_error_screenshot. The scope is well-defined and oriented to data visualizations.

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 for data visualization images but provides no explicit when-to-use vs alternatives, prerequisites, or exclusions. The context is clear enough from the resource list, but there is no direct guidance on choosing this tool over analyze_image or understand_technical_diagram.

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