get_forecast

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

The description discloses the output format (Low/Mid/High) and the demo-mode limitation (synthetic data). However, it omits behavioral details such as authentication requirements, side effects, or error scenarios. With no annotations, a higher bar is unmet.

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 sentences long, front-loading the core purpose and a key caveat. Every sentence adds value with zero waste.

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?

The tool has 4 parameters, no output schema, and no annotations. The description covers the return type but omits important context such as when the optional token is needed, error handling, or idempotency details.

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?

All four parameters have descriptions in the schema (100% coverage), so the baseline is 3. The description adds no new semantics beyond the schema; it does not explain parameters like token or client_request_id further.

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 verb 'Get', the resource 'coarse availability forecast (Low/Mid/High)', and the scope 'for a product family and period'. It is distinct from sibling tools like discover_products or create_intent, making selection unambiguous.

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?

No explicit guidance is provided on when to use this tool versus alternatives. The note about 'Demo mode: synthetic data only' hints at a limitation but does not advise on appropriate contexts or exclusion criteria.

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