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

The annotations already state readOnlyHint=true, idempotentHint=true, destructiveHint=false, and openWorldHint=false. The description adds useful behavioral context beyond them: chiffre_estime is an estimate rather than register data, comparison uses the preceding equal-length period, and curve granularity has automatic thresholds. It does not discuss auth or rate limits, but those are less central for this annotated read-only 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 front-loaded with scope and metrics, then usage examples, then sibling disambiguation. The first sentence is dense but every clause maps to a parameter or returned concept, and there is no 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?

For a seven-parameter analytics tool with no output schema, the description lists the main returned indicators and explains period, curve, comparison, and alternatives. It does not fully describe the response structure or every indicator, but the schema is complete for parameters and annotations cover the safety profile.

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%, and the schema itself documents all seven parameters, including enum values, defaults, date formats, taux_retour, and chiffre_estime definitions. The description adds high-level context but little syntax or semantics beyond what the schema already provides, so the baseline of 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?

The description names the resource (a commerce loyalty dashboard) and enumerates the metrics, period, curve, and comparison it returns. It also distinguishes the tool from chercher_clients and rentabilite, so an agent can identify it as an analytics query rather than a client lookup or profitability tool.

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?

It gives concrete user questions such as « comment ça marche ce mois-ci ? », « combien de clients en août ? », and « est-ce que ça monte ? ». It also explicitly redirects client lists to chercher_clients and card ROI to rentabilite, providing clear when-to-use and alternatives.

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