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

Discloses traits beyond the annotations: result shape (match count, top 2 skills with license and install command), the existence of a gated full ranked list at $9, and "read-only, no account." The paid upsell-and-truncation behavior is important context an agent cannot get from the schema or annotations. Minor gap: doesn't say how to interpret low/zero match counts or whether the top-2 results are guaranteed.

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 scope and corpus size, then query examples, then return shape, then safety note — a logical progression with no filler. The pricing parenthetical is slightly salesy but is genuinely relevant to result truncation, so it earns its place.

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?

For a single-parameter, no-output-schema tool, the definition covers scope, query style, return shape, truncation/upsell, and safety. The main missing piece is routing guidance relative to the two sibling tools, which leaves a real decision to inference.

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% and the single query parameter is documented in-schema with the same example phrasing, so the description is largely redundant here. The example list ("stripe webhooks", "playwright tests", "django admin", "terraform aws") reinforces that the query is free-text task/tool phrasing, which adds slight value. 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 (search), the resource (90,454 open-license agent skills / SKILL.md), and the axis of search (task, tool, framework) with concrete query examples. This clearly distinguishes it from list_skill_tags (browsing taxonomy) and recommend_skills (suggestion rather than query-driven lookup).

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?

Usage is implied by the examples ("stripe webhooks", "playwright tests"), so an agent can infer this is keyword-style lookup. However, no explicit when-to-use vs. when-not guidance and no mention of the siblings list_skill_tags or recommend_skills, so the agent must infer which of the three to call.

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