feature_to_point

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

With no annotations provided, the description carries the full burden. It states that the tool reads in_features and writes a new output, implying no modification of input, but lacks details on permissions, error handling, or constraints beyond the schema.

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 a clear structure: action, use cases, process. It is concise and front-loaded with the core purpose, though it could be slightly more compact.

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 input schema richness and assumed output schema, the description covers the essential purpose and typical usages. It does not explain output details, but the output schema is separate. For a tool with multiple parameters, the description is adequate.

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?

The input schema has detailed descriptions for all parameters (e.g., in_features, out_features, overwrite, point_location), covering format and constraints. The tool description adds little beyond restating the basic process, so a baseline score is appropriate given high schema coverage.

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 clearly states the tool creates representative point features from polygons or lines, specifying the method (ArcPy FeatureToPoint) and common use cases (centroids, inside points for labeling, joins, sampling). This distinguishes it from sibling tools like feature_vertices_to_points.

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 when to use the tool (for centroids or inside points, labeling, joins, sampling) but does not explicitly exclude cases or mention alternatives. The context is clear, and sibling tool names are available for differentiation.

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