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Trakt

by io.github.pipeworx-io

List Seasons

list_seasons
Read-onlyIdempotent

"How many seasons of [show]" / "list seasons of [TV series]" / "episode counts per season" — list all seasons of a show with episode counts. Pass extended="episodes" to include each episode in the response.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
show_idYestrakt ID or slug
extendedNofull | episodes | full,episodes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
countYesNumber of items returned.
itemsYesArray of seasons for the show

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior. The description adds value by explaining that passing extended='episodes' includes episode data in the response, which is behavioral detail beyond annotations. No contradictions.

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?

Two sentences efficiently convey purpose and a key parameter detail. Front-loaded with example queries. No unnecessary words; every sentence earns its place.

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?

The tool is simple with two parameters, and an output schema exists, so return values are covered. The description covers the main use case and the extended option. Omits details like default ordering or limits, but with the output schema and annotations, it's sufficiently complete for this tool.

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%, with descriptions for both parameters. The description further clarifies the extended parameter by specifying the exact value ('episodes' and 'full,episodes') and its effect. This adds meaningful context beyond the schema, justifying above baseline.

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?

Description clearly states the tool lists seasons of a show with episode counts, using specific examples like 'How many seasons of [show]' and 'list seasons of [TV series]'. It distinguishes from sibling tools like get_show and get_episode by focusing on aggregate season data, making purpose unambiguous.

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?

Examples explicitly show when to use the tool (querying season counts or lists). However, it lacks explicit guidance on when not to use it or mention of alternatives like get_episode for individual episodes. Still, the context is clear enough for an agent to infer typical usage.

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.

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