Count matching features

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

Adds useful behavior beyond the readOnlyHint: it is 'Cheap' and intentionally does not return features. This helps the agent understand cost and results without contradicting the annotations.

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 concise sentences, each earning its place: purpose first, then cost/use-case, then input options. No redundant or filler content.

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 count operation, the description covers the key invocation decisions and area semantics. Since there is no output schema, it could mention the exact return type, but the count semantics are clear enough from the name and description.

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 coverage is only 25%, but the description compensates by explaining the two complementary input forms: point (lat, lon, radius_m) versus bounding box (south, west, north, east). This adds grouping and mutual-exclusivity semantics not present in the schema.

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?

States a specific action ('Count'), resource ('OpenStreetMap features'), and scope ('matching tag filters in an area'). Adding 'without returning them' clearly distinguishes it from sibling tools that fetch features.

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 guidance: 'Use this before pulling data when the result size is unknown.' This gives a concrete adoption context, though it does not explicitly name sibling alternatives or state when not to use it beyond what is implied.

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