map_identify_at_point

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

With no annotations, the description carries the full burden. It discloses the bounding-box tolerance for vector layers, pixel value return for rasters, default CRS behavior, and caps on feature counts/attribute lengths (citing `get_layer_sample`). This provides good insight into the tool's operation, though the exact pixel tolerance value is not specified.

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?

The description is three sentences with no extraneous words. The first sentence gives the core purpose, the second explains behavior by layer type, and the third adds defaults and constraints. It is well-structured and efficient.

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?

Given the tool's complexity (dual vector/raster behavior, multiple visible layers) and the presence of an output schema, the description covers essential aspects: scope, tolerance method, default parameter, and performance caps. Minor omissions (e.g., exact tolerance, output structure) do not detract significantly.

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 0%, but the description adds meaning: it explains that `crs` defaults to canvas CRS and implies that `x` and `y` are map coordinates. This compensates for the lack of parameter descriptions in the schema, though it does not detail coordinate system or output format.

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 states 'Identify features at a point, across every visible layer,' which clearly specifies the verb (identify), resource (features) and scope (point, all visible layers). This distinguishes it from sibling tools like `get_layer_sample` (layer-specific) and `features_select_by_*` (different selection methods).

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 explains the tool's behavior for vector vs raster layers and notes that `crs` defaults to canvas CRS. It implies usage for map identification at a point, but does not explicitly state when not to use it or provide direct alternatives. However, the context is clear and useful for discriminating from similar tools.

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