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

Annotations declare readOnlyHint=true, and the description adds 'LLM-ranked' to indicate AI-based ranking, which goes beyond annotations. No contradictions. Slightly more detail about visibility could be included, but it's well covered.

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 concise sentences: the first defines the tool's primary function, the second clarifies its limitations and alternative. No extraneous words.

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?

Given the tool's moderate complexity, the rich schema, and annotations (readOnlyHint=true), the description is largely complete. The presence of an output schema compensates for lack of return value details. Could mention that results are ranked and what 'visible to you' means precisely, but still adequate.

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?

With 100% schema description coverage, the schema already documents all parameters (tag, limit, query, filter) thoroughly. The description adds only a high-level phrase ('Natural-language search request over skills visible to the caller') without new parameter-specific details. 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?

The description clearly states 'LLM-ranked natural-language search over skills visible to you,' establishing a specific verb (search), resource (skills), and method (natural language, LLM-ranked). It distinguishes itself from sibling tools like list_skills by mentioning lexical prefiltering.

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 explicitly says 'This does not perform lexical query prefiltering. Use list_skills with search=... for deterministic metadata filtering.' This provides clear when-to-use and when-not-to-use guidance with an alternative tool.

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