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describe_location

Identify the region at any coordinate and sample its elevation, reporting ground, built structures, or off-map/ocean areas.

Instructions

Name the region at a coordinate, with confidence, and sample its elevation.

Returns 'off-map or ocean' rather than guessing the nearest land region.

There is no heightmap in any data this reads, so elevation is a SAMPLE and is reported with its count and spread rather than as one invented number. Resource nodes rest on terrain and are quoted as ground; foundations and buildings are quoted separately as built elevation, because a platform is wherever the player put it. Where the two disagree, the difference is the fill already stacked there.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
x_mYes
y_mYes
saveNo
worldNo
radius_mNohow far to look for known elevations, metres
Behavior5/5

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

With no annotations, the description carries full transparency weight and excels: it discloses the off-map/ocean behavior, explains the elevation is a sample with count/spread due to no heightmap, and details how ground vs built elevation differences indicate fill. This is honest and avoids overstating precision.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is efficiently structured: it leads with the core purpose, then provides necessary behavioral caveats in separate sentences. Every sentence adds value, covering edge cases and precision limitations without redundancy. Length is justified by the complexity of what it reports.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Without an output schema, the description sufficiently explains what the tool returns (region name, confidence, elevation sample count/spread, ground vs built). It also covers the 'off-map or ocean' edge case and the fill interpretation. The only gap is incomplete parameter documentation for save/world, but the description is strong for a read-only query tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is only 20% (radius_m has a description). The description mentions 'coordinate' which implies x_m/y_m but does not explain save, world, or radius_m. It adds meaning to x_m/y_m but leaves three parameters (save, world, radius_m) essentially unexplained beyond schema defaults.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb ('Name') and resource ('region at a coordinate') and clearly states the dual function (region identification + elevation sampling). It distinguishes itself from sibling tools like whereami or list_regions by explicitly targeting arbitrary coordinates and returning 'off-map or ocean' instead of guessing.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies when to use the tool (when you need region/elevation at a coordinate) but does not explicitly list alternatives or state when NOT to use it. It lacks explicit guidance like 'use instead of whereami when you have arbitrary coordinates' which would elevate it from implied to clear context with exclusions.

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

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