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

With no annotations provided, the description carries the burden, and it does well by disclosing the two-stage behavior: YouTube Data API search followed by Muse Spark AI ranking by relevance, credibility, and content quality. It also sets expectations for output with 'curated shortlist with reasoning.' It does not mention the x_payment prerequisite, though the schema already marks it required.

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?

The description is two sentences with no fluff. The core action is front-loaded in the first sentence, and the second sentence adds just enough mechanism detail without restating the schema.

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 values do not need explanation. The description covers purpose, methodology, ranking criteria, and output style. The only missing piece is explicit mention of the x_payment requirement, but that is clearly documented in the schema, so the tool is adequately specified for selection and invocation.

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?

The input schema has 100% coverage and describes search_term, num_videos, and x_payment. The description adds no parameter-level details beyond the schema, but the mention of relevance ranking indirectly reinforces that search_term is the core query. Baseline 3 is appropriate since the schema handles parameter semantics.

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?

The description opens with a specific verb and resource: 'Search YouTube and return AI-ranked results with relevance reasoning.' It clearly states the tool's function, the ranking mechanism, and the output format, and it is distinct from siblings like video_preview or get_video_intelligence because it emphasizes AI ranking and a curated shortlist.

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 gives clear context: use this tool when you need to search YouTube and get AI-ranked, relevance-reasoned results. It does not explicitly name alternatives or exclusion conditions, but the 'AI-ranked' and 'curated shortlist' wording helps disambiguate from raw search or channel-analysis siblings.

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