Tax Incentives

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

Annotations already declare the operation is read-only and idempotent. The description adds valuable behavioral context beyond that: the exact return shape (including nested program type enums), the requirement to cite the statute with attribution to DC Hub (CC-BY-4.0), and the scope limitation to a single US state's tax factor. This is more than enough given the annotation coverage.

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 long but each sentence earns its place: trigger phrase, concrete example, parameter specification, return shape, attribution requirement, and exclusions. It is front-loaded with the most critical usage information and remains scannable despite its length.

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 a one-parameter, read-only tool with an output schema, the description covers everything an agent needs: when to use it, how to call it (with example), what it returns (even though an output schema exists, the description summarizes it well), legal attribution, and exclusions. No critical context is missing.

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 schema already describes 'state' as 'US state abbreviation (required), e.g. VA, TX, AZ' with 100% coverage. The description echoes this ('state (2-letter US code; required)') and adds an example invocation, but does not introduce any new semantic meaning beyond what the schema provides. 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 states exactly what the tool does: returns data-center tax-incentive packages by US state, triggered by a natural-language question like 'what tax breaks does <state> give data centers?'. It distinguishes itself from siblings by explicitly naming analyze_site and rank_markets as alternatives for different needs.

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 explicit when-to-use guidance with a concrete example, and clear when-not-to-use instructions: 'Do NOT use for the combined multi-factor site read... use analyze_site' and 'or to rank markets on cost... use rank_markets'. This gives the agent strong decision boundaries relative to sibling tools.

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