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youtube_suggest

Get YouTube search-box autocomplete suggestions for a query prefix, ranked in YouTube's own order. Localize results with hl and gl language and region codes.

Instructions

Suggest YouTube search queries. Returns YouTube search-box autocomplete completions for a query prefix, in YouTube's own ranking order. hl and gl localize the list; they default to en and US.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qYesSearch query prefix
glNoTwo-letter YouTube region code; defaults to US
hlNoYouTube interface language, such as en, de, or pt-BR; defaults to en
countNoSuggestions to return; defaults to 10, clamped to 1..20

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.17.9

TDQS

A3.5/5.0
Behavior3/5

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 two genuinely useful behaviors: results come back in YouTube's own ranking order and the list is localized. It does not mention any rate limits, auth requirements, or the exact shape/count of the returned list, leaving gaps for a zero-annotation tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two tight sentences with the core function front-loaded and localization detail trailing. No wasted words, though the second sentence largely restates defaults already present in the schema.

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?

With no output schema, the description does supply the key return-value context (autocomplete completions in YouTube ranking order) plus localization behavior, which is what an agent needs to call it correctly. Minor omissions like the count cap are covered by the schema.

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%, so the four parameters (q, gl, hl, count) are already documented, including the en/US defaults. The description merely repeats the localization role of hl and gl and adds no syntax or format detail beyond the schema, so the baseline of 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource: returns YouTube search-box autocomplete completions for a query prefix. The nature of the output is clear enough (suggestions, not full search results) to separate it from a video-search tool, but it never names the sibling (youtube_search) that an agent would otherwise confuse it with.

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 description implies the use case (feed a query prefix, get completion suggestions) but gives no explicit when-to-use guidance and no exclusions or alternatives. An agent must infer that this is for query expansion/autocomplete rather than retrieving videos.

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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