get_training_suggestions

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool is 'AI-powered' and based on 'recent activities and performance', but doesn't describe what the suggestions entail, how they're generated, whether they require specific permissions, or what the response format looks like. For a tool with no annotation coverage, this leaves significant gaps in understanding its behavior.

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 a single, efficient sentence that front-loads the core purpose without unnecessary details. Every word earns its place, making it easy to scan and understand quickly. No waste or redundancy is present.

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?

Given the tool's moderate complexity (AI-powered suggestions with 2 parameters), no annotations, and no output schema, the description is minimally adequate. It covers the basic purpose but lacks details on behavior, usage context, or output format. It meets the minimum viable standard but has clear gaps in providing a complete picture for an agent.

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 100% description coverage, with clear documentation for both parameters (activityType and days), including enums and defaults. The description doesn't add any parameter-specific information beyond what the schema provides, such as explaining how 'activityType' influences suggestions or what 'days' means in practice. Baseline 3 is appropriate since the schema does the heavy lifting.

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 purpose: 'Get AI-powered training suggestions based on recent activities and performance'. It specifies the verb ('Get'), resource ('training suggestions'), and basis ('recent activities and performance'). However, it doesn't explicitly differentiate from sibling tools like 'get_activity_insights' or 'get_workouts', which might also provide related information.

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 provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'get_activity_insights' or 'get_workouts', nor does it specify prerequisites (e.g., needing authentication or recent activity data). Usage is implied by the purpose but lacks explicit context or exclusions.

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