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

With no annotations provided, the description carries the full burden and does so well. It discloses the data source, radius, output units, signed receipt, per-call cost, and the free-first-5 pricing model. These are meaningful behavioral details beyond a generic 'get weather' statement.

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?

Two sentences deliver the core behavior, parameters, constraints, and pricing with no filler. The most important information is front-loaded, and every clause adds value.

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 read-only weather lookup with three optional parameters and no output schema, the description covers input methods, output fields, measurement units, relay proximity, receipt, and pricing. Nothing essential is missing for an agent to invoke it 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 description coverage is 100%, so the schema already documents all three parameters. The description adds the high-level instruction 'Pass latitude and longitude, or a city,' but does not introduce new semantics like precedence when both are supplied or city resolution behavior. This matches the baseline of 3.

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 identifies the tool as returning current weather metrics (temperature, humidity, pressure, wind) for a place, and adds a concrete sourcing constraint (nearest live Open-Meteo relay within 75 km). This is specific enough to distinguish it from siblings like air_quality_now and nearby_sensors.

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 gives clear usage context: provide latitude/longitude or a city, and the tool resolves to the nearest relay within 75 km. It also mentions cost and a free tier, which helps callers decide when to call. It does not explicitly name alternatives or exclusion conditions, but the weather-specific wording makes the intended use unambiguous.

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