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

Annotations already indicate readOnlyHint=true, and the description adds useful behavioral context: it mentions the response is bounded ('Réponse bornée') and that each suggestion includes its meaning and semantic associations (pairWith). This goes beyond the annotation without contradicting it.

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 two concise sentences, front-loaded with the primary purpose. Every phrase adds value, no redundancy.

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 two parameters and no output schema, the description provides essential context: the tool's semantic reasoning, output structure (meaning, pairWith), and bounded response. It could mention the default/max limit explicitly, but the schema covers that, making it sufficiently complete.

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 100%, providing baseline 3. The description adds value by giving a concrete example of the 'need' parameter ('un dashboard avec des métriques et un graphique'), clarifying expected input beyond the schema's generic description.

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 suggests a list of Blueprint Modular components based on a natural language need, using the semantic layer. It distinguishes itself from siblings (list_components, search_components, get_component) by specifying its semantic reasoning and the screen-intention use case.

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 explicitly says to use the tool when starting from a screen intention (e.g., 'un dashboard avec des métriques et un graphique') to obtain relevant components. It provides clear context but does not explicitly name alternatives or state when not to use this tool.

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