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Get Show

get_show
Read-onlyIdempotent

"TV show info for [title]" / "details on [series]" / "how many seasons does [show] have" / "is [show] still on the air" / "[show] genre / runtime / cast" — full TV show record by Trakt ID, slug, or IMDb ID. Pass extended="full" for runtime, genres, cast, rating, airs schedule, network, status.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYestrakt ID, slug, or IMDB/TMDB ID
extendedNofull | metadata | images

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
idsNoTrakt, IMDB, TMDB, Slug IDs
airsNoAir schedule information
yearNoYear started
titleNoShow title
votesNoNumber of votes
genresNoList of genre slugs
peopleNoCast and crew information
ratingNoAverage rating
statusNoreturning series or ended
runtimeNoEpisode runtime in minutes
languageNoLanguage code
overviewNoPlot summary
first_airedNoFirst air date
translationsNoAvailable translations

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already indicate read-only, idempotent, non-destructive. Description adds details about return content and ID formats, but no mention of rate limits or error handling. Adds value beyond annotations.

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

Conciseness4/5

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

Front-loaded with example queries, followed by core description. Somewhat lengthy but each part contributes value. Could be slightly more structured.

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

Completeness4/5

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

With output schema present, return values are covered. Description provides typical use cases and parameter details for the two inputs. Adequately complete given the tool's simplicity.

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

Parameters4/5

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

Schema coverage is 100%, baseline 3. Description explains 'extended="full"' returns runtime, genres, cast, rating, etc., and that 'id' accepts Trakt ID, slug, or IMDb ID, adding meaning beyond schema.

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 retrieves TV show info, provides typical user queries, and specifies ID types. It distinguishes from siblings like get_episode and get_movie by focusing on show-level data.

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?

Example queries imply when to use (e.g., 'details on [series]'), but no explicit 'when not to use' or direct alternatives. Siblings provide context but without explicit guidance.

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

A3.8/5.0
Disambiguation2/5

The tool set has significant overlap among query and research tools (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, bet_research) and among entity/company tools (entity_profile, compare_entities, recent_changes). Despite detailed descriptions, an agent would struggle to select the correct tool without careful reading, especially for nuanced differences.

Naming Consistency2/5

Tool naming is inconsistent: some start with verbs (ask_, generate_, validate_, scan_, subscribe) while others are nouns (entity_profile, popular, trending, search, recent_alerts, recent_changes). The snake_case style is consistent, but the verb_noun pattern is not, making predictions of tool names difficult.

Tool Count3/5

38 tools is on the high side for a single server, but the scope is broad (general query, research, Trakt, subscriptions, memory). The count is appropriate for the wide range of functionality, though some tools could be merged to reduce cognitive load.

Completeness4/5

The tool set covers a very wide range of tasks: querying, research, entity profiles, comparisons, subscriptions, memory, Trakt operations, etc. For the Trakt domain, it has all essential operations (search, get, list, trending). The Pipeworx side has a comprehensive set for data access, grounding, and validation. Minor gaps exist (e.g., no update for subscriptions), but overall it is well-covered.