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

Goes well beyond the annotations by disclosing the 10,000-point cap, the billing cost of 1 unit per 1,000 points, and the auth requirement (API key or connected account). Annotations only cover safety and idempotency, so cost and credential prerequisites are genuinely new and decision-relevant information.

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 sentences with the core behavior, the use cases, and the operational constraints in that order. No filler, no restatement of the title, and the cost/auth caveats come before the agent commits to a call.

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 only two parameters and no output schema, the description still covers return shape (boolean per point plus inside count), input limits, pricing, and authentication. There is no material gap an agent would need to guess at before calling.

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% and the parameter-level descriptions already spell out the boundary id formats and the [lon, lat] GeoJSON ordering, so the schema does the heavy lifting. The description adds only the point-count ceiling and the 'one boundary per call' constraint, so baseline 3 applies.

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 precise verb and resource: testing up to 10,000 [lon, lat] points against a single boundary, with both the boolean-per-point result and the inside count named. It is clear enough to separate from generic boundary tools, but it never distinguishes itself from reverse_boundaries, which is the obvious alternative for point-in-boundary style lookups in this toolset.

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?

Gives concrete usage contexts (geofencing, service-area checks, filtering customer lists) so an agent knows the class of problem this solves. It stops short of exclusions or naming a sibling to use instead, which keeps it out of 5 territory.

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