maestro_get_recommendations

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

No annotations are provided, so the description carries full burden. It describes that the tool uses an AI engine and returns recommendations, but it does not disclose whether it is read-only, requires authentication, or any other behavioral traits. It is not contradictory but lacks depth.

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 fairly concise with a front-loaded purpose statement. It includes a brief explanation of the engine, return fields, and an example. However, the example includes non-English text, which may slightly reduce clarity.

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 tool with no parameters and no output schema, the description covers the tool's purpose, how it works, and the return structure with an example. It is largely complete, though it could mention error handling or prerequisites.

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 input schema has zero parameters, and schema coverage is 100%. Per calibration, baseline is 4. The description adds value by explaining the return structure and example, which compensates for the lack of parameters.

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 states the tool gets smart design recommendations based on user preferences, specifying the resource (recommendations) and the verb (get). It mentions factors like user preference history and project context, but does not explicitly differentiate from sibling tools like maestro_get_decision or maestro_get_analytics.

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 implies usage context with the example 'Get recommendations before starting a session', but it does not provide explicit when-to-use or when-not-to-use guidance, nor does it compare with alternatives.

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