compare_provinces

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

No annotations, so the description carries the full burden. It clearly explains the ranking behavior (highest first), parameter ranges, and purpose. It doesn't explicitly mention non-mutating behavior, but the action is read-only by nature and the response format is handled by the output schema. Slightly more detail on edge cases could push to 5.

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 brief sections: purpose, args, usage. No wasted words. Every sentence adds value, and the structure is easy to scan.

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 is simple with 2 optional parameters, an output schema exists, and the description provides usage context, parameter semantics, and downstream tools. Fully adequate for an AI agent to invoke correctly.

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 has zero description coverage, but the description explains both parameters: horizon_weeks ('1 to 8') and top_n ('how many provinces to return, highest forecast first'). This fully compensates for the schema gap.

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 'Rank provinces by forecast incidence to support prioritisation' with a clear verb (Rank) and resource (provinces). It distinguishes itself from siblings like `forecast_incidence` by focusing on cross-province comparison for prioritization.

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?

Provides an explicit use case ('where should we pre-position resources') and names follow-up tools (`forecast_incidence`, `detect_outbreak_signals`) to drill into a single province. This gives clear when-to-use and alternative context.

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