recommend_film

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

With no annotations, the description carries the full behavioral burden. It implies a read-only recommendation but says nothing about return format, whether results are ranked, how many are returned, or any constraints. This is a significant gap for a zero-annotation tool.

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?

A single front-loaded sentence with no filler; the parenthetical scene list adds required information rather than padding. Slightly dense but efficient.

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?

For a 3-param tool with no annotations and no output schema, the description covers the core intent and scene values but omits format vocabulary, price tier semantics, and any sense of what the response contains.

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 0%, so the description must compensate. It usefully enumerates the accepted 'scene' values (portrait, landscape, street, night, travel, studio, beginner), which the schema omits, but leaves 'format' options and the meaning/units of 'max_price_tier' unexplained.

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 (recommend) and resource (film stocks) with a scoping qualifier ('for a scene'). It doesn't explicitly distinguish itself from the sibling search_film_stocks, but the recommendation framing itself implies a different behavior than search.

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 implies the discovery/recommendation use case and enumerates valid scenes, but never states when to prefer this over search_film_stocks or get_film_stock. Usage context is inferred rather than declared.

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