mean_center

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

No annotations are provided, so the description must carry the full burden. It discloses that it writes a point feature class, uses ArcPy MeanCenter, and respects PathGuard allowed roots. However, it does not mention potential side effects like overwriting or performance considerations for large datasets.

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 two sentences long, front-loaded with the main purpose, and includes essential use cases and parameter hints. Every sentence adds value, and there is no wasted wording.

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 (statistical calculation with optional weighting and grouping), the description covers the main points and typical uses. An output schema exists, so return values are documented separately. Could mention that output includes fields for coordinates and groups.

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 provides descriptions for all parameters (100% coverage), so the baseline is 3. The description adds context about PathGuard allowed roots and optional weight/case fields, but does not add significant meaning beyond what the schema already provides.

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 that the tool calculates the geographic mean center using ArcPy MeanCenter and writes a point feature class. It specifies the resource (mean center) and verb (calculate), and distinguishes from siblings by focusing on central tendency summary for incidents, facilities, etc.

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 provides typical use cases (summarizing central tendency of various spatial features) and mentions optional parameters. However, it does not explicitly state when not to use this tool or suggest alternatives like directional_distribution for dispersion.

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