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search_youtube

Search YouTube for videos matching a query and return their metadata as structured JSON, including title, channel, URL, duration, views, and publish date.

Instructions

Search YouTube for videos matching a query and return a list of matching videos with their metadata.

Performs a live YouTube search over the network, so results reflect current YouTube data and may vary between calls; it is read-only and has no side effects. Returns a list of video objects, each typically containing: title (str), channel (str), url/link (str) to the video, video_id (str), duration (str), view_count (int), and published/upload date (str). Returns an empty list when the query matches no videos.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesThe search text, as a string. Example: "model context protocol tutorial". No default (required).
max_resultsNoMaximum number of videos to return, as an integer. Example: 10. Default 20.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataNo
countNo
errorsNo
scraperNo
source_urlsNo
Behavior5/5

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

With no annotations provided, the description carries the full burden of disclosure. It explicitly states that the operation is read-only, has no side effects, performs a live network search, and results may vary between calls. It also describes the edge case of returning an empty list for no matches. This is transparent and goes beyond minimal expectations.

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: it opens with a clear purpose, then adds behavioral context, then describes the return format, and finally addresses an edge case. Every sentence contributes useful information without redundancy or unnecessary length.

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?

Given the tool's moderate complexity, the presence of an output schema, and full parameter schema coverage, the description is remarkably complete. It explains purpose, network behavior, safety (read-only), return fields, and empty-list behavior. No critical information needed for correct invocation or interpretation is missing.

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?

The input schema already provides 100% description coverage, including examples and defaults for both 'query' and 'max_results'. The description text does not add significant parameter-specific meaning beyond referencing the query. Since the schema handles parameter semantics well, a baseline score of 3 is appropriate, and the description does not need to compensate.

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 verb 'Search' and the resource 'YouTube', and specifies the action: searching for videos matching a query and returning metadata. This distinguishes it from sibling tools like search_images or search_github, which target different resources and use different verbs.

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 mentions that it performs a live YouTube search over the network, providing context that results are current and non-deterministic. However, it does not explicitly state when to use this tool versus alternatives, nor does it mention any exclusions or scenarios where another tool would be preferable. The usage context is implied but not fully articulated.

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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