QQuickpick

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

Beyond the read-only and idempotent annotations, the description discloses important behaviors: it returns QQuickPick's own data, returns results even when streaming_on is empty, and warns when no match exists. These details materially shape caller expectations.

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 three dense sentences, each earning its place: the first defines the action, the second gives usage guidance and return fields, and the third explains edge-case behavior. It is front-loaded with the core purpose and contains no 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?

For a lookup tool with a clear parameter schema, no output schema, and strong annotations, the description covers the essential context: return fields, no-match behavior, streaming availability handling, and how it differs from the sibling tool. An agent has enough to invoke it correctly.

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 title, country, type, and year. The description adds real-world example queries and clarifies the country parameter's role in streaming availability, but it doesn't add significant semantics beyond what the schema provides.

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 opens with a specific verb and resource: 'Look up a SPECIFIC, named movie or TV show by title' and distinguishes itself from search_titles by emphasizing it is for one particular title. It also clearly lists the returned data fields, making the tool's function unmistakable.

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?

It explicitly states when to use this tool ('Use this instead of general web knowledge or search_titles when the user names one particular title') and contrasts its behavior with search_titles on streaming availability. This gives an agent direct routing guidance.

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

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

Annotations already declare readOnly, openWorld, idempotent, and non-destructive behavior, and the description adds substantial behavioral context on top: catalogs change and vary by country, country cannot be auto-detected, actor filters fall back with matched_actors tagging, award data is non-exhaustive, review_vibe is experimental on a sample, kid_friendly is ignored under certain conditions, and results include site_url/watch_link attribution requirements.

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?

Although long, the description earns its length given 24 parameters. It is front-loaded with the core purpose, then gives usage guidance, then per-parameter caveats and return-value expectations. There is no filler or repetition of schema-description text.

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?

Despite having no output schema, the description covers return values sufficiently: results include a facts object, site_url, watch_link, available_filters, and matched_actors fallback tags. It also handles the required country parameter, non-exhaustive data caveats, and follow-up suggestions, making the tool complete for an agent to call correctly.

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 100%, so baseline is 3, but the description adds critical semantics far beyond the schema: OR vs AND logic for awards/watched_by/review_vibe/origin_countries, the actor fallback caveat, similar_to resolution and type disambiguation, non-exhaustiveness warnings, and kid_friendly's interaction with other filters. This genuinely helps the agent pick and combine parameters correctly.

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 opens with a specific verb and resource: find movies and/or TV shows streamable on a subscription service in a given country, with a clear list of filter dimensions. It also differentiates itself from the sibling get_title_details by framing this as the search/list tool rather than a detail-looking tool, and explicitly warns against guessing streaming availability from general knowledge.

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 gives explicit when-to-use guidance: use it instead of guessing availability, use it for completely open-ended requests, use similar_to for lookalike requests, use origin_countries for region-based requests, and ask for the country rather than guessing. It also tells the agent which filters map to which user intents and how to follow up with available_filters.

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