suggest_assets_for_post

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It accurately states the tool fetches AI-suggested assets from the brand library, but it does not disclose any potential limitations, requirements (e.g., need for existing brand assets), or whether the operation is read-only. No contradiction exists, but the transparency is limited.

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, front-loaded sentence of about 15 words. It immediately states the action ('Get') and provides the essential qualifiers ('AI-suggested', 'brand library', 'match a given post context or topic'). Every word earns its place with no redundant filler.

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?

The tool is relatively simple (3 params, 1 required) but has no output schema and no annotations. The description explains what the tool does but does not clarify what the response looks like (e.g., returns a list of asset IDs/metadata) or any edge cases (e.g., what happens if no assets match). While adequate for basic invocation, it leaves room for ambiguity.

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 67% (brandId and context have descriptions, but limit does not). The tool description does not add any additional semantics for the parameters; it merely paraphrases the context parameter ('post context or topic') and does not explain the limit parameter's behavior (e.g., number of suggestions returned). Thus, it fails to compensate for the missing limit documentation.

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 a specific verb ('Get') and resource ('AI-suggested assets from the brand library') with a clear qualifier ('that match a given post context or topic'). It clearly distinguishes this tool from siblings like list_assets or search_stock_images by emphasizing AI-driven suggestion based on context.

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 implies the tool should be used when a user needs asset suggestions for a given post context or topic. It does not explicitly name alternative tools or exclusions, but the context is clear from the phrasing 'AI-suggested assets' and 'brand library'.

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