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rotten-tomatoes.tv_show_season_reviews

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

Returns review quotes, sentiment, publication, critic details, and pageInfo for pagination. Use cursor from pageInfo.endCursor for the next page. Optionally set type to critic for critic reviews only.

Cost = 5 tokens.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum number of review pages to return (1–50, default 20).
cursorNoPagination cursor from a previous response pageInfo.endCursor field.
review_typeNoReview filter. Use critic for critic reviews; omit for the default set.
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 number.
reviewsNoReview entries with quote, sentiment, critic, and publication.
pageInfoNoPagination metadata for review listings.

Schema Changelog

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

  1. First observed

TDQS

A4.4/5.0
Behavior4/5

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

No annotations are provided, so the description carries the burden. It discloses the return fields (quotes, sentiment, publication, critic details), pagination mechanics via pageInfo.endCursor, and cost. This is sufficient for a read-only load operation, though it omits rate limits or error conditions.

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 compact and front-loaded: the first sentence states the primary function, followed by return details, pagination, optional filter, and cost. Every sentence adds useful information without redundancy.

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?

Given the presence of an output schema and comprehensive parameter schema, the description covers the essential behavioral aspects: what is returned, how to paginate, and the optional critic filter. It does not explain default limit behavior in text, but the schema already provides that, making this sufficiently complete.

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%, so baseline is 3. The description adds semantic value by giving a concrete example for tv_show_slug, explaining how cursor pagination works, and clarifying the review_type filter. This goes beyond merely listing parameter names.

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 loads reviews for a TV season using a series slug and season number, with examples. It distinguishes itself from sibling tools like movie_reviews and tv_show_details by specifying season-level granularity.

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 on input format (e.g., series slug like 'breaking-bad') and optionally restricting to critic reviews. It does not explicitly mention when to avoid this tool, but the use case is unambiguous enough for agents.

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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