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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, closed-world). The description adds useful non-structured context: results are 'precomputed', the recommendation to prefer median/winsor_mean over mean, and the cost_wages PLN-only constraint. No gaps that would mislead.

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-loads the purpose, followed by the return set and usage examples. Efficient overall, though the inline list of returned statistics is dense; it earns its place given there is no output schema.

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 compensates by enumerating the returned aggregates (median, p25/p75/p90, mean, total, n_companies, min/max), which is exactly what an agent needs. All parameters are documented in the schema, so nothing critical is missing.

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 100%, so the schema already documents metric, currency, dimension_key, and dimension_type with enums and the PLN-only note. The description echoes the cost_wages constraint and adds the 'prefer median' guidance, but adds little syntax beyond the schema. 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 and resource: 'Get precomputed aggregate financial statistics' for sectors/regions/PKD branches/countries. The word 'aggregate' implicitly distinguishes it from the company-level get_financials sibling, but it never names that alternative, so an agent must infer the boundary.

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?

Provides concrete example questions ('what is the median revenue / average profit / assets in industry X or region Y?') that clearly signal when to reach for this tool. It stops short of naming when-not-to-use or pointing to an alternative like get_financials for company-level data.

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