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

Annotations already cover readOnly, idempotent, and non-destructive behavior. The description adds meaningful behavioral context: the heat score is 'shrunk toward the platform average so a single lucky job cannot outrank a real market,' and advises reading 'heat' next to 'confidence.' This goes beyond the annotations and helps the agent interpret results safely.

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 purpose and metrics, then usage guidance, then sibling differentiation. Every sentence earns its place with no redundancy or fluff.

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?

With no output schema, the description must explain what is returned—it does, listing the metrics and the heat score. It also covers how to interpret the data (dense supply vs. supply holes) and provides enough context for correct invocation. Combined with the sibling reference, it is complete for an agent to use the tool 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 covers 100% of parameters, so baseline is 3. The description adds value by explaining the 'openUnanswered' sort surfaces supply holes and suggesting minJobs=5+ for established markets. These details enrich parameter meaning beyond the schema's basic descriptions.

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 finds US cities and their job-market activity metrics (posts, applications, response share, median hours). It names the specific resource and distinguishes itself from check_service_availability, making its purpose unmistakable.

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?

It explicitly states when to use this tool: 'Use this to decide where supply is dense (spend more) versus where jobs go unanswered (a supply hole).' It also differentiates from a sibling by noting check_service_availability counts locals rather than measuring response. It doesn't list explicit exclusions, but the guidance is strong and contextually sufficient.

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