analyze_image

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

Annotations already declare readOnlyHint and idempotentHint, providing the base safety profile. The description adds valuable context about the reliability of vision-extracted data versus text tables and explains the dependency on image_key from read_datasheet, which are behavioral traits not covered by annotations.

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: it front-loads the purpose, then provides usage guidance, an important caveat, and examples. It is slightly long but every sentence contributes value, and the formatting with an IMPORTANT section and examples improves scannability.

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 there is no output schema, the description adequately conveys what the tool returns through examples (e.g., classifies and describes the image). It also covers the workflow dependency on read_datasheet and the trade-offs between vision and text extraction, making it sufficiently complete for correct invocation.

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%, so parameter basics are already documented. The description enhances this with usage examples showing how image_key and question interact, and clarifies that image_key is the storage path from read_datasheet output, adding relational meaning beyond the schema.

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 from datasheets using vision AI, with a specific verb ('Analyze') and resource (image from a component's datasheet). It distinguishes itself from read_datasheet by specifying it handles visual data like graphs, package drawings, pin diagrams, and circuit schematics.

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 when to use this tool (when read_datasheet returns images needing extraction) and when not to use it (for precise numeric values, where read_datasheet text tables are preferred). It names the alternative tool, read_datasheet, and provides concrete examples of appropriate use cases.

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