forecast_demand

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

No annotations provided, so description must convey all behavioral traits. It mentions 'AI-powered' and 'Requires API key' but omits details on response types, rate limits, latency, error handling, or whether it can handle multi-part questions. The description is insufficient for safe invocation.

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?

Three sentences, each adding distinct value: core function, capabilities, and constraints. No redundancy or wall of text. Efficiently front-loaded.

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 an AI forecasting tool with no output schema, the description lacks details on return format (e.g., confidence intervals, time series data), error states, or prerequisites beyond API key. Incomplete for agent to gauge results.

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%, so baseline is 3. The description adds an example for query parameter, which is helpful. Hemisphere parameter is well-defined in schema. No further elaboration needed, but no extra value beyond schema.

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?

Description clearly states it forecasts demand using natural language questions, specifying target areas: inventory needs, sales trends, supply chain patterns. While it distinguishes from generic tools like natural_language_command, it could more explicitly contrast with siblings like detect_bottlenecks or get_optimization_recommendations.

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 guidance on when to use this tool versus alternatives. The 'Premium tool. Requires API key.' hints at access but does not explain conditions or exclusions. Siblings suggest multiple ways to query data, but no comparative advice.

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