forecast_data

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

No annotations are provided, so the description bears full responsibility. It mentions the method ('regression') and lists return fields (forecast_data, growth_rate, etc.), which informs about outputs. However, it does not disclose assumptions, limitations (e.g., short data), or the existence of a method parameter (linear/exponential) beyond the schema.

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 brief and to the point, with no wasted words. The quote adds a touch of context. Could be slightly improved by removing the quote to save space, but it remains concise.

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 complexity (5 parameters, output schema), the description covers the basic purpose and output structure but lacks details on parameter choices (e.g., linear vs exponential) and interpretation of outputs like r_squared, which might be needed for proper use.

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%, so the schema already documents all parameters. The description adds no extra meaning to the parameters themselves; it only provides overall context. Baseline 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 clearly states the action ('Forecast future values using regression'), identifying the resource (future values) and the method. It is distinct from sibling tools like 'benchmark_data' or 'compare_datasets' but does not explicitly differentiate itself.

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 quote 'At this rate, X by 2030' hints at a typical use case, but there is no explicit guidance on when to use this tool vs alternatives, or when not to use it. Sibling tools like 'compute_metrics' or 'aggregate_data_tool' could overlap, but no comparison is made.

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