identify_card_from_image

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

No annotations are provided, so the description carries the burden. It labels the tool as a 'compatibility' endpoint, which hints at legacy or fallback behavior but doesn't elaborate. It provides minimal behavioral context beyond the recommendation to prefer another tool. The 'compatibility' framing suggests it may be deprecated or degraded, but this isn't explicit.

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, compact sentence that front-loads the key decision point (which tool to prefer). It's efficient and wastes no words, though it could add a bit more detail without becoming bloated.

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 annotations, 0% parameter coverage, and no guidance on the 5 parameters, the description leaves significant gaps for an image-identification tool with dual input modes (path vs base64). The output schema exists, which covers return-value understanding, but parameter semantics and behavioral expectations for this 'compatibility' endpoint are underspecified.

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 0%, so the description must compensate for 5 undocumented parameters. The description names the endpoint purpose but provides no parameter-specific guidance (e.g., when to use image_path vs image_base64, what language_hint accepts, role of visual_verify, top_k semantics). Given 0% coverage, this is a notable gap despite the tool name suggesting clear function.

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 identifies this as an 'image-identification endpoint' with a specific verb (identify) and resource (card from image). It distinguishes from the sibling 'analyze_pokemon_card' by explicitly recommending that tool instead. However, it doesn't fully specify what 'identify' returns (candidates, confidence scores, etc.) beyond what the output schema conveys.

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 provides clear usage guidance: it's a 'compatibility' endpoint and explicitly states to 'Prefer analyze_pokemon_card' for natural-language card-photo requests. This names the alternative tool. However, it doesn't state when this tool SHOULD be used (i.e., the compatibility scenarios where it's the right choice), only when to avoid it.

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