skill_stats

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

No annotations are provided, so the description carries the full burden. It explains the aggregate nature of the result, mentions share and median salary, and clarifies that no vacancy list is returned. It does not describe edge cases like empty filters or pagination, but the core behavior is disclosed.

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, front-loaded with the result, then use cases, then an exclusion with a sibling tool. Every sentence adds value and there is no redundancy.

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?

The description is complete for purpose and routing, and an output schema exists, so the return shape is covered elsewhere. However, with four parameters completely undocumented and no annotations, the description is not fully sufficient for an agent to construct a correct call confidently.

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 0%, and the description does not explain what query, top_n, area_id, or published_within_days mean semantically. It only refers vaguely to "фильтр" (filter). An agent receives almost no guidance on how to populate these parameters correctly.

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?

Description clearly states the tool computes skill statistics: which technologies appear most often in matching vacancies, their share, and median salaries for vacancies requiring each skill. It also distinguishes itself from search_vacancies by explicitly saying it does not return a vacancy list.

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 tells the agent when to use this tool: for questions like "what is currently in demand" or "what to learn". It also gives an exclusion and names the alternative tool, search_vacancies, for retrieving vacancy lists.

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