Analyze Images in Order

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

Annotations already declare read-only, idempotent, and non-destructive behavior. The description adds valuable context such as resizing, strict processing order, and the use of specific external models (Gemini, OpenAI, OCR), which justifies the expected behavior. It doesn't mention potential network dependencies or rate limits, but the core behavioral traits are disclosed.

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, dense sentence that front-loads the action and follows a logical sequence (resolve, resize, analyze, return). Every clause contributes information, with no filler 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 only 2 parameters and no output schema, the description provides a fairly complete picture of the workflow and the return format ('delimited JSON context'). It could be more specific about the JSON structure and any prerequisites, but given the annotations and simplicity, it's adequate.

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 descriptions already cover both parameters at 100% coverage. The description enriches the meaning by specifying that image_paths are local/OpenCode references, that order matters for analysis, and that images are resized—details not present in the schema. It also ties the prompt to the analysis focus implied by the pipeline.

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 ('Resolve', 'resize', 'analyze') and names the resources (local/OpenCode image paths) and the processing pipeline (Gemini, OpenAI, OCR). It clearly distinguishes from sibling tools like read_code_context and read_zip_context, which target code and zip files, not images.

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 clearly states the tool is for analyzing images and outlines the ordered process, making it obvious when to use it. It doesn't explicitly exclude other tools, but the sibling names (read_code_context, read_zip_context) show they serve different content types, so the context is clear.

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