analyze_image

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

No annotations are provided, so the description bears full responsibility for behavioral disclosure. It explains the tool invokes an LLM and defaults to detailed description, but does not mention limitations (e.g., image size, format compatibility, or error handling). Some behavioral context is implied but not fully detailed.

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 two concise sentences, front-loading the core function and unique input constraints. Every word adds value without redundancy. It is efficiently structured for quick comprehension.

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 10 parameters and no output schema, the description covers the main functionality and key parameters but lacks details on output format, error handling, or performance characteristics. It is adequate for basic use but leaves gaps for complex scenarios.

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 high (80%), but the description adds critical semantic value by explaining the mutual exclusivity of path/url/base64 and the role of the prompt parameter. This goes beyond the schema's individual descriptions, providing context on how parameters relate and behave.

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 analyzes images using a vision LLM, specifies the three supported input formats (path, URL, base64), and mentions optional prompt customization. The verb 'analyze' combined with 'image' and the mention of specific use cases (OCR, table extraction) makes the purpose unambiguous.

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 instructs the user to provide exactly one of path, url, or base64, which is a clear usage constraint. It also notes the optional prompt to steer analysis. While no siblings exist to differentiate, the guidance is direct and actionable, though it lacks explicit when-not-to-use scenarios.

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