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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint=false and destructiveHint=false, covering the safety profile. The description adds the ranking criteria and the fact that reasoning accompanies each result, but omits notable traits: the mandatory x402 payment payload and the latency cost of a two-stage fetch-then-rank pipeline.

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

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

Two front-loaded sentences that lead with the core action and then explain the pipeline and output. No filler, though the second sentence is slightly verbose about ranking criteria.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

An output schema exists, so return shape needn't be documented, and annotations cover the safety profile. The remaining gap is that the description never signals the required x402 payment payload, which is a genuine prerequisite an agent must satisfy before calling.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100% across all three parameters, so the schema already documents search_term, num_videos (default 5, max 20), and the x_payment payload. The description adds no parameter-level detail, so the baseline 3 is appropriate.

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

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

States a specific verb chain (search, fetch, rank) and resource (YouTube videos), and explains the two-stage mechanism (YouTube Data API discovery + Muse Spark AI ranking on relevance/credibility/quality). This clearly differentiates it from browsing-oriented siblings like trending_on_youtube, though it never names an alternative to route against.

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

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

The description implies the use case (curated, ranked recommendations) but gives no explicit when-to-use guidance, no prerequisites, and no comparison to siblings such as trending_on_youtube, niche_research, or keyword_autocomplete. An agent must infer selection criteria entirely.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.