read_image

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds genuine value beyond that: the server's inability to read text, the unique return type (image block instead of Pydantic model), and the pipeline consequence (no stage/next_actions). This is meaningful behavioral disclosure that annotations alone don't convey, though slightly less than a full 5.

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?

Purpose is front-loaded in the first sentence, and every paragraph earns its place: usage caution, return-type distinction, and next-step routing. It's efficient but slightly long — the Pydantic-model mechanism detail is useful but could arguably be trimmed without losing the core message.

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 the tool's relative simplicity (2 params, full schema coverage, rich annotations), the description covers purpose, usage caveats, return format, and routing to the correct sibling. The lack of an output schema is compensated by the explicit statement that it returns an image block. Nothing critical is missing.

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 description coverage is 100% — both path and max_dim are already documented with type, constraints, and default. Description does not add parameter-level semantics beyond the schema (the token-cost tradeoff on max_dim is already in the schema description). Baseline 3 is appropriate since the schema carries the full weight here.

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?

States a specific verb and resource ('delivers raster images as-is') and is explicit about what it does NOT do — the server can't read text in images, so the agent must judge the image itself. It clearly distinguishes itself from siblings by noting it returns an image block rather than a Pydantic model, and names analyze_structure as the related alternative.

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?

Gives explicit when-to-use guidance ('use this tool to receive the image directly and judge for yourself'), a when-not-to rule (don't read numbers from unlabeled charts — mark as '[확인 필요]'), and names the sibling for the next step (analyze_structure for resolution/EXIF). Usage routing is fully spelled out.

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