get_recommendations

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the tool returns similar movies and is 'Great for suggestions,' but lacks details on permissions, rate limits, pagination behavior (beyond the schema's page parameter), or what happens with invalid movie IDs. For a tool with no annotation coverage, this is a significant gap in describing behavioral traits.

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 appropriately sized and front-loaded, with two sentences that efficiently convey the core purpose and usage context. Every sentence earns its place by adding value: the first defines the tool, and the second clarifies its practical application. There is no wasted text 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?

Given the tool's moderate complexity (2 parameters, no output schema, no annotations), the description is adequate but incomplete. It covers the purpose and basic usage but lacks behavioral details like error handling or output format. Without annotations or an output schema, the description should do more to compensate, but it meets a minimum viable level for clarity and differentiation.

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%, so the schema already documents both parameters (movie_id and page) fully. The description adds no additional meaning beyond what the schema provides, such as explaining the recommendation algorithm or typical output size. With high schema coverage, the baseline score of 3 is appropriate as the description doesn't compensate but also doesn't detract.

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 clearly states the tool's purpose with specific verbs ('Get movie recommendations based on a specific movie') and distinguishes it from siblings by specifying it's for 'If you liked X, try Y' suggestions, unlike tools like get_movie_details or search_movies. It explicitly identifies the resource (movie recommendations) and the basis (specific movie).

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 context for when to use this tool ('based on a specific movie' for 'If you liked X, try Y' suggestions), which differentiates it from siblings like get_trending (general trends) or search_movies (keyword-based). However, it doesn't explicitly state when not to use it or name specific alternatives, such as get_tv_recommendations for TV shows.

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