QQuickpick

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

With no annotations provided, the description carries full responsibility for behavioral disclosure. It states the tool returns QQuickPick's own data including score, runtime, genres, streaming availability, and for movies the 'facts' object. It also transparently explains that streaming_on may be empty if not available and that no result is returned with a warning if nothing matches. No annotations contradict this.

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 dense but well-structured, front-loading the core purpose and then adding distinctions and edge cases. It is longer than strictly necessary but every sentence carries functional value, and it avoids 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 four parameters and no output schema, the description covers what the tool returns, how it differs from the sibling, and the failure behavior. It also implies the need for a country parameter. Nothing essential is missing for an agent 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 all four parameters. The description adds examples (e.g., 'Practical Magic') and explains the tool's overall purpose, but does not add meaning beyond what the schema provides for individual parameters. Baseline 3 is 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?

The description clearly states the tool looks up a specific named movie/TV show by title, with concrete examples. It explicitly distinguishes itself from search_titles by positioning it as the choice when a user names one particular title, rather than a discovery tool.

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 says 'Use this instead of general web knowledge or search_titles when the user names one particular title' and contrasts its behavior with search_titles (returns results even if not streaming, whereas search_titles filters by availability). This gives clear when-to-use and when-not-to-use 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?

With no annotations provided, the description carries the full burden and does so thoroughly. It discloses AND/OR logic, automatic fallback behavior for actors, non-exhaustive awards data, experimental review_vibe sampling, kid_friendly interaction caveats, the inability to auto-detect country, and the available_filters follow-up hint.

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 long but earned: it covers 24 parameters and many cross-parameter interactions. It front-loads the core purpose and the most important anti-guessing guidance, then flows into parameter-specific rules. Minor structural improvement would be bullets or shorter paragraphs, but each sentence adds value.

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 complex 24-parameter tool with no output schema and no annotations, the description is remarkably complete. It covers parameter semantics, edge cases, interaction warnings, fallback behavior, data quality caveats, and even response elements like site_url, watch_link, and available_filters. An agent has enough context to invoke it correctly in varied conversational scenarios.

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 79%, and the description still adds significant meaning beyond the schema: country must be asked not defaulted, actors fall back with matched_actors, awards values are OR with data caveats, kid_friendly conflicts are resolved with warnings, and origin_countries is explicitly about production origin, not streaming region. This goes well beyond the baseline.

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 genuinely available on subscription streaming in a specific country. It clearly distinguishes the tool from general-knowledge guessing and from the sibling get_title_details by framing this as the search/exploration tool.

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 for streaming availability rather than guessing, use for open-ended requests, use similar_to for 'something like X', and use origin_countries for regional requests. It also states when NOT to guess and instructs the agent to ask the user for the country, which is critical operational guidance.

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