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

Annotations already cover the safety profile (readOnlyHint, idempotentHint, openWorldHint=false), and the description still adds real value: corpus size, multilingual query support, and the return shape ('ranked cards with a Markdown link to read next'). It stops short of saying whether results are truncated, how ranking works, or that full content requires a second call.

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?

Front-loaded with the core purpose, then capabilities, return shape, and the call trigger — a sensible ordering. The capability enumeration is dense but each item (skills, tutorials, recipes, formats, prompts, styles) signals real scope rather than padding; only the triple example list runs slightly long.

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?

With no output schema, the description carries the return-value burden and does so ('ranked cards with a Markdown link to read next'). Combined with an explicit usage trigger and 100% schema coverage on three simple params, an agent has everything needed to call this correctly.

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 coverage is 100%, so the baseline is 3, but the description earns above it by clarifying that `query` is free-form task language in any language (with Cyrillic and English examples) and by enumerating the content types that map onto the `kind` enum. It says nothing extra about `limit` or the 'all but single presets' default behavior, which remains schema-only.

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 ('searches') plus the exact corpus (2,000+ agent skills, tutorials, FFmpeg recipes, video formats, prompts, styles) and the intent ('find how to make any AI video'). The mention of returning 'cards with a Markdown link to read next' implicitly signals the follow-up fetch step but never names the sibling get_how_to, so the search-vs-fetch distinction is left to inference.

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?

'Call it before improvising a video pipeline' gives a concrete trigger condition, and the three quoted example queries show the expected granularity. There are no explicit exclusions or a named alternative (get_how_to) for when the agent already knows the item it wants, which keeps this below a 5.

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