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Recommend short dramas

recommend_shows
Read-onlyIdempotent

Recommend short drama series on BingeTube for a mood, trope or story the person likes (e.g. "revenge after divorce", "werewolf romance", "hidden billionaire", "something like ReelShort"). Returns shows with links to watch them on BingeTube.

Input Schema

NameRequiredDescriptionDefault
genreNoOptional genre
likesYesWhat they want to watch, in their words
limitNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.2/5.0
Behavior3/5

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

Annotations already declare readOnly, idempotent, non-destructive and closed-world, so the safety profile is fully covered. The description adds that results come back as shows with links to watch on BingeTube, which is useful given there is no output schema, but it says nothing about result count defaults, ranking basis, or behavior when no match is found.

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: the first front-loads the action and the audience intent, the second covers the return value. The examples earn their space by showing the expected input register.

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?

For a simple three-parameter read tool with no output schema, the description covers intent, input style and output shape adequately. Remaining gaps (default limit, ranking logic, empty-result behavior) are minor but real given the absence of an output schema.

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 67% and the description meaningfully clarifies the key required parameter 'likes' as free-form natural language ('in their words'), supported by worked examples. The genre enum is self-documenting in the schema, but 'limit' is never mentioned in the description despite lacking a schema description.

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?

States a specific verb (recommend) and resource (short drama series on BingeTube) and grounds it with concrete example inputs like 'revenge after divorce' and 'something like ReelShort'. This is clearly distinguishable from the retrieval siblings get_show/search_shows/top_shows, which find or list rather than suggest.

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 phrase 'for a mood, trope or story the person likes' gives a clear triggering context, and the examples reinforce the kind of fuzzy, natural-language request this tool is built for. It does not explicitly say when to prefer search_shows or top_shows instead, so the alternative routing is left implicit.

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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