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

Annotations already declare readOnly, idempotent, and non-destructive. The description adds that results are sorted and include all states. However, it claims 'all 50+ states' while annotations include openWorldHint=true, which could imply potential incompleteness. No contradiction arises given the small dataset, but it's a minor ambiguity.

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 three sentences, front-loaded with the core action, and efficiently communicates purpose, filters, output, use case, and source without extraneous words.

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 simple read-only tool with an output schema, the description covers essential aspects: what it does, what parameters, output summary, and use case. It lacks explicit guidance on when to use alternatives, but overall it is sufficiently complete for an 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?

Input schema has 100% coverage with descriptions for both parameters. The description only reiterates the award_type enum values and fiscal year concept, adding no new semantic depth beyond what the schema already provides.

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 tool returns total US federal spending by state, filtered by award type and fiscal year, and indicates the output is all 50+ states sorted by amount. It distinguishes itself from sibling tools (e.g., spending.federal.agency) by focusing on geographic aggregation.

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 description mentions use cases ('regional economic analysis and policy research') but does not explicitly guide when to choose this tool over siblings or provide exclusions. Usage is implied rather than explicit.

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