spatial_autocorrelation

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

With no annotations, the description carries full burden. It states 'returns scalar statistics, writes no dataset,' which is a key behavioral disclosure (non-destructive read). However, it does not cover required data types, side effects, or if the input is modified, leaving gaps.

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 a single sentence that efficiently conveys the core output and a safety trait (no dataset written). It is front-loaded and avoids redundancy, though it could benefit from slight structural expansion for readability.

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 complexity of a spatial autocorrelation tool with multiple parameters (distance band, conceptualization, etc.) and no annotations, the description does not adequately prepare the agent. It lacks guidance on parameter selection, data prerequisites, and expected output format despite an output schema existing.

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% for individual parameters. The tool description adds no parameter-level meaning beyond the schema's type/name/enum information. The agent lacks guidance on how to use fields like 'distance_band' or 'conceptualization' in context.

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 computes Global Moran's I index, z-score, and p-value for spatial autocorrelation. This distinguishes it from siblings like hotspot_analysis (Getis-Ord Gi*), making purpose unambiguous.

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 implies usage for spatial autocorrelation analysis but offers no explicit when-to-use or when-not-to-use guidance. No alternatives or prerequisites are mentioned, leaving the agent to infer context from the tool name and statistical output.

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