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

With no annotations provided, the description carries the full behavioral burden. It adds valuable context by stating that the computation is local, involves no network call, and consumes no quota. It does not cover edge cases like empty input, but the '1+' constraint mitigates that concern.

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 short sentences deliver the output format, primary use case, and key performance trait without any filler. The essential definition is front-loaded, making it easy for an agent to parse quickly.

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 pure local computation with a single well-documented parameter and an output schema, the description is complete. It explains what the tool returns, why an agent would use it, and that it has no network or quota implications. Nothing critical is missing for correct invocation.

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 input schema already documents the single `points` parameter at 100% coverage, including the LatLon structure. The description adds little parameter-level meaning beyond the '1+' minimum, so the schema does the heavy lifting. This matches the baseline score for high schema coverage.

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 defines the tool as computing an axis-aligned bounding box and explicitly gives the output shape {min_lat, min_lon, max_lat, max_lon}. This distinguishes it from sibling geo tools like geo_centroid, geo_area, and geo_distance with specific, non-tautological language.

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 provides a concrete use case: fitting a map view to a set of stops. It does not explicitly name alternatives or say when not to use this tool, but the context is clear and actionable enough for an agent to select it appropriately among geometry siblings.

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