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

Annotations already declare readOnly/idempotent/non-destructive, so the description focuses on what annotations can't convey: the operation runs offline, is priced at $0.002 USDC per successful call via x402 on Solana, and has an HTTPS mirror endpoint. The cost and offline guarantees are exactly the kind of behavioral context an agent needs before invoking a paid tool; only return-shape/pagination-style detail is missing.

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?

Capability list is front-loaded immediately after the verb and the payment/endpoint metadata is correctly relegated to the end. It is dense and every clause carries information, though the long parenthetical blocks make the single capability sentence slightly hard to scan.

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?

There is no output schema, and the description compensates fully by naming each returned field and its units (m², km², ha, acres), the area formula used, the contents of the validity report, and the optional hull/simplified geometry. Cost, transport, and offline behavior are all disclosed, leaving nothing an agent needs before calling it.

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 100%, so the baseline is 3, but the description adds meaning the schema does not: point-in-polygon tests honour polygon holes and edges, the hull is taken from the geometry or from the points when no geometry is supplied, and simplification uses a metre-based Douglas-Peucker tolerance. These clarify how the four parameters interact rather than restating their types.

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?

Starts with a specific verb + resource ('Analyse GeoJSON polygons') and enumerates the accepted input types and computed outputs (geodesic area, perimeter, centroid, bbox, validity, point-in-polygon, hull, simplification). It is unmistakably distinct from geometric siblings like geo_distance, heron_area, or coord_convert, which compute a single scalar.

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 makes the operating envelope clear: accepted GeoJSON shapes, a 1,000-point cap for point-in-polygon, a 0.01–100000 m tolerance for simplification, and the hull/points interaction. It does not explicitly say when to prefer a sibling such as geo_distance or heron_area instead, so it falls short of a 5.

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