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

No annotations are provided, so the description carries the full behavioral burden. It does so thoroughly: it discloses data sources (ANAF + ONRC), precomputation, the ≥30-firm threshold for percentiles, fallback behavior via `usedNationalFallback`, the `smallSegment` flag, the exact-one-of-two segment rule, and that the tool is free ('Gratuit'). The agent knows what will happen, including edge cases, before calling.

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 well-structured with clear sections: keyword block, main definition, parameter constraints, usage examples, and disambiguation. It is longer than average, and the opening keyword list is somewhat repetitive with the body, but every section adds operational value. The main behavior and exclusions are front-loaded early.

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 4-parameter, no-output-schema tool with no annotations, this description is exceptionally complete. It explains what is returned, how the segment is uniquely selected, what optional filters do, when fallbacks occur, and which sibling tools to use instead. An agent has enough information to invoke it correctly and interpret the important edge cases without any missing context.

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?

Although input schema coverage is 100%, the description adds meaning the schema does not: `caenCode` must be a 4-digit class code and 2–3 digit codes are explicitly unsupported, `industry` must come from the 34 Companero industries with `list_industries` as a fallback, and the two are mutually exclusive. It also clarifies that `year` affects only counts while percentiles remain on the reference year. This is substantive semantic guidance beyond the schema descriptions.

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 definition: 'Statistici AGREGATE pentru un segment de piață din România' and lists exact return values (active/new/dissolved firms, P25/median/P75 percentiles, multi-year trend). It distinguishes itself from siblings in the 'Disambiguare' section, explicitly contrasting with search_companies, list_industries, get_cpv_market, and get_financial_health. This is far beyond a vague restatement of the tool name.

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 'Când îl folosești' section provides concrete natural-language queries that map directly to this tool, such as 'Care e cifra de afaceri mediană a firmelor de software din Cluj?' and 'Câte firme s-au înființat / radiat în construcții anul acesta?'. The 'NU confunda cu' section explicitly says when to prefer alternative sibling tools, e.g., search_companies for individual companies. No usage context is left to inference.

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