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rotten-tomatoes.search

Search Rotten Tomatoes by title or name.

Returns matching movies, TV series, and celebrities with slugs, scores, poster URLs, release years, and top-billed cast where available.

Cost = 13 tokens.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum number of search results to return (1–50, default 20).
queryYesSearch query for movies, TV series, or celebrities.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryNoEcho of the search query.
moviesNoSearch hits (movies, TV series, or celebrities). Each entry includes type, slug, title, url, posterUrl, score, and cast when available.

Schema Changelog

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

  1. First observed

TDQS

A4/5.0
Behavior4/5

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

With no annotations, the description properly carries the burden. It goes beyond a bare action by disclosing the returned fields (slugs, scores, poster URLs, release years, top-billed cast) and even the 13-token cost. It omits pagination and rate-limit details, but those are less essential for a read-only search operation.

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 tight and well-ordered: the action first, then the return payload, then the cost. Every sentence earns its place, and there is no redundant or vague wording.

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 simple two-parameter schema, an output schema, and the read-only nature of a search, the description is largely complete. It tells the agent what to expect and what it costs. The main gap is not mentioning that returned slugs can be passed to the sibling rotten-tomatoes detail tools, but that is a convenience rather than a correctness issue.

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%: both query and limit are described directly in the input schema. The description reinforces that the query is a title or name and covers movies, TV, and celebrities, but it adds no new parameter-level meaning beyond what the schema already provides.

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 states a specific action ('Search Rotten Tomatoes') with a clear resource and scope ('by title or name'). It also lists what it returns — movies, TV series, and celebrities along with useful fields — making it easy to distinguish from sibling detail/cast/review tools that operate on an already-known entity.

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

Usage Guidelines3/5

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

The intended usage is implied: this is the discovery/search tool for Rotten Tomatoes entities. However, it never explicitly tells the agent when to prefer this over the sibling detail tools or to chain the returned slug into movie_details/tv_show_details/celebrity_details.

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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