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

Annotations only declare readOnlyHint/openWorldHint, so the description adds real behavioral context: a per-call cost of 10 credits, the DVF 2014–2025 data cadence with a months ≥ 12 reliability caveat, and a constraint the schema does not encode (at least one location filter is required despite zero required params).

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-loaded with purpose and routing, then required/optional/returns/example/notes in scannable sections; every block carries information. The Returns list and example add some length, but no sentence is filler.

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 fully describes the return payload (volumes with YoY, trend direction, activity level, seasonality, market signal) and also covers cost, data coverage, and the implicit required-filter rule. An agent has everything needed to call 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?

Schema coverage is only 20%, so the description must compensate — it documents the commune example ('PARIS 11'), the role of code_departement/code_postal as alternative location filters, type_local as a property-type filter, and the months default/max. It does not clarify the distinction between code_postal and code_departement or behavior when multiple filters are combined, so it falls short of full compensation.

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+resource ('Monthly transaction volume time series') and immediately distinguishes itself from the closest sibling by naming score_market_health and the question each answers. An agent can pick between them without opening either schema.

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?

Explicit when-to-use vs when-not-to-use, with concrete trigger questions ('show me how volume has evolved' / 'is there seasonality?' vs 'is this market good or bad?'), plus the alternative tool named for the other case. Nothing 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.