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

With no annotations, the description carries the full burden of behavioral disclosure. It explains that every figure reflects the query exactly as given, that NIR is normalized to 'year' regardless of the requested frequency, and that NIR is omitted if the product lacks a PRODCOM mapping. It also fully describes the return structure, which is comprehensive for a read-only analytical tool.

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 longer than average but well-structured: it opens with the core purpose, then gives usage guidance, and finishes with a detailed return-structure breakdown. Each section adds value, and the bullet-style list under narrative_facts is readable. It could be slightly trimmed without losing information, but it is far from bloated.

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?

Despite the tool's complexity (nested query, multiple output components), the description is complete: it covers purpose, usage, behavioral nuances (NIR normalization/omission), return fields, and how to adjust parameters. It even explains the tool's relationship to sibling tools. An agent has everything needed to select and invoke it correctly.

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?

The schema already documents all parameters at 100% coverage, so the baseline is 3. However, the description adds practical, tool-specific semantics: it recommends setting query.frequency to 'year' for multi-year summaries and explains that changing query.partner_set is the way to contrast different partner groups. These are meaningful usage insights beyond the schema's generic field 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 clearly states the tool's function as a 'Report-section helper' that produces a 'condensed, report-ready digest of how a market has evolved', and it explicitly differentiates itself from the siblings get_overview, get_reporter_detail, get_concentration, and get_net_import_reliance by saying it replaces the need to call them separately. It is a specific verb+resource with clear scope.

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 gives clear when-to-use guidance: for the 'General overview' report section, and recommends setting query.frequency to 'year' for multi-year evolution. It also provides an alternative usage path by telling the agent to call again with a different query.partner_set for different slices, explicitly stating it does not substitute a different reporter/partner_set. This is more than a generic usage context.

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