SceneF — San Francisco Movie Showtimes

Plan a movie night

scenef_plan_movie_night

Read-only

The concierge: give it a window and a taste profile and it returns 2-4 complete plans — film + specific showtime + theater + why it fits — each with ticket and calendar links, plus one wildcard pick outside the stated genres. Rankings are pure preference-fit; never pay-ranked. In "detailed" mode every showtime also carries its confidence level, source tier, reporting sources, and verified_at timestamp.

Input Schema

TableJSON Schema

Name	Required	Description
`when`	No	When to look: "tonight" (default), "tomorrow", "weekend" (Fri/Sat/Sun of the current week), or a YYYY-MM-DD date.
`party_size`	No	How many people are going.
`preferences`	No	Bring-your-own taste profile. Everything here tilts the ranking and degrades gracefully EXCEPT time_after/time_before, which are hard bounds.
`response_format`	No	Output size: "concise" (default) for tight text lines, "detailed" to add ids, per-showtime ticket urls, and extra metadata.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`note`	Yes
`plans`	Yes
`nights`	Yes
`window`	Yes
`wildcard`	Yes
`data_as_of`	Yes
`party_size`	Yes
`plan_count`	Yes
`attribution`	Yes
`accuracy_url`	Yes
`discounts_relaxed`	Yes

TDQS

A4.5/5.0

Behavior5/5

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

Annotations already declare readOnlyHint and destructiveHint, but the description adds significant behavioral detail: rankings are pure preference-fit and never pay-ranked, and detailed mode includes confidence levels, source tier, reporting sources, and timestamps. It also mentions a wildcard pick and that time bounds are hard (though that is in the schema). This goes well beyond annotation defaults, so it fully carries the burden of behavioral disclosure.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences with zero fluff. The first sentence front-loads the purpose and output, the second explains the ranking philosophy and detailed mode. Every sentence earns its place; no redundant clauses.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a complex tool with nested preferences and an output schema, the description covers the key aspects: what plans look like, ticket/calendar links, wildcard behavior, ranking policy, and detailed-mode metadata. The hard-bound behavior is in the schema descriptions, so it is not missing. The description is fully sufficient for an agent to understand what it does and when to call it.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with thorough descriptions for all parameters, including nested preferences and the hard-bound semantics of time_after/time_before. The description summaries this as 'a window and a taste profile', which adds minimal extra meaning. Per the baseline rule, when schema fully covers parameters, a 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's function: it takes a window and a taste profile and returns 2-4 complete movie plans with film, showtime, theater, and reasoning. The output format (plans, links, wildcard) is specific, and the 'concierge' framing distinguishes it from sibling tools like search_showtimes or now. It is a clear, resource-specific purpose.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives clear context on what inputs to provide ('give it a window and a taste profile') and what to expect (plans with links). It does not explicitly mention when to avoid this tool in favor of a sibling, but it implies usage for the specific task of planning a movie night. This is a clear usage context without explicit exclusions, earning a 4.

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

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TDQS

A4.1/5.0

Disambiguation4/5

Most tools have clearly distinct query shapes: film lookup, theater card, discount list, coming-soon radar, and recommendation planner are easy to separate. Some overlap exists because several tools expose showtimes from different angles (e.g. scenef_whats_playing vs. scenef_now, and scenef_film_details vs. scenef_search_showtimes), but the descriptions provide enough grounding that an agent can usually select correctly.

Naming Consistency3/5

Every tool is prefixed with scenef_ and uses snake_case, which gives a consistent visual identity. Nevertheless, the pattern after the prefix is mixed: some names are nouns (scenef_discounts, scenef_accuracy), some are time references (scenef_now, scenef_coming_soon), and some are verb-first (scenef_search_showtimes, scenef_plan_movie_night). It is readable but not a single predictable verb_noun convention.

Tool Count5/5

Nine tools is right in the ideal range and each tool earns its place in the SF showtimes domain. The set covers discovery, detail lookups, discounts, recommendations, and data-accuracy verification without feeling padded or bloated.

Completeness4/5

The domain is covered broadly: film discovery, film details, theater details, per-film showtimes, coming soon, discounts, nightly overview, personalized planning, and accuracy metrics. The main gap is that there is no simple cross-film showtimes-for-a-specific-date listing, but agents can usually work around this with scenef_whats_playing and scenef_search_showtimes.

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