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

Annotations already declare readOnlyHint=true and openWorldHint=true, lowering the bar, yet the description adds genuine behavioral context beyond them: 'Credential-free public Letterboxd data,' telling the agent no authentication is required. It does not address rate limits or return shape, but the output schema covers return values.

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?

Three short sentences, front-loaded with the core purpose followed by the auth note and the parameter hint. Every sentence earns its place with zero 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?

With an output schema present (return values need no explanation) and annotations covering the safety profile, the description supplies purpose, auth status, and slug sourcing. It is complete enough for a one-parameter read tool, though it omits any pagination or data-scope caveats.

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% for the single slug parameter, so the schema already documents format and example. The description merely restates the same slug guidance and example ('parasite-2019') without adding new meaning, making the baseline 3 appropriate.

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 (returns) and resource (star-rating distribution) with precise scope: '0.5 to 5.0 in half-star buckets with counts and percentages.' This clearly differentiates it from sibling letterboxd_film_reviews (text) and letterboxd_film_similar, so an agent can tell what it yields without opening the schema.

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?

Provides a concrete prerequisite/routing hint: 'Use letterboxd_film to get a Letterboxd film,' telling the agent how to obtain the required slug. However, it does not explicitly tell the agent when to prefer this histogram over letterboxd_film_reviews or other film-data siblings, so the when-not guidance is absent.

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