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rotten-tomatoes.tv_show_season

Load season-level details for a TV series by slug (for example breaking-bad) and season number.

Returns season title, episode list summaries, and season Tomatometer scores when available.

Cost = 5 tokens.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tv_show_slugYesTV series slug (for example breaking-bad).
season_numberYesSeason number as one or two digits (for example 1 or 01).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
slugNoTV series slug.
seasonNoSeason identifier.
episodesNoEpisodes in the season when listed.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.3/5.0
Behavior4/5

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

No annotations are provided, so the description carries the transparency burden. It discloses the return contents (season title, episode list summaries, Tomatometer scores when available) and the token cost, adding value beyond just 'load'. It implies a non-destructive read operation, though it does not explicitly state side-effect freedom or error behavior. Given the tool's simplicity, this is solid coverage.

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 concise and well-structured: the first sentence states the primary purpose, the second summarizes key return data, and the third notes the cost. No unnecessary fluff, and the most important information is front-loaded.

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 tool with only two parameters and no nested objects, the description is complete: it states the inputs, the type of data returned, and the cost. An output schema is present, so not explaining the full return structure is acceptable. The description is sufficient for an agent to select and call the tool correctly.

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 description coverage is 100%, with both parameters already well-described in the input schema. The description adds minimal extra meaning beyond reinforcing the slug example and season format; it mostly restates what the schema provides. Baseline 3 is appropriate because the schema handles the heavy lifting and the description does not compensate beyond that.

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 uses a specific verb 'Load' and clearly identifies the resource (season-level details for a TV series) and inputs (slug and season number). It distinguishes from sibling tools like tv_show_details and tv_show_episode by explicitly focusing on season-level data, making its 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?

The description provides clear context for use: when you need season-level details for a TV series, identified by slug and season number. It does not explicitly name alternatives or exclusions, but the season-level scope implicitly differentiates it from show, episode, and review tools. No when-not-to-use guidance is given, so it falls short of a 5.

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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