spatial_autocorrelation

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

With no annotations, the description discloses that the tool only reads in_features and does not create or modify datasets, which is key for safety. It does not detail other potential behaviors like license requirements, but covers the main non-destructive aspect.

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?

Two concise sentences that front-load the verb and resource, with no wasted words. The structure is efficient and clear.

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 and lack of annotations, the description adequately covers its purpose, usage context, output (scalar statistics), and non-destructive nature. It omits details like output schema structure, but that is covered by the existing output schema.

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 description adds no additional parameter information beyond what is already provided in the input schema, which itself includes detailed descriptions for each parameter. Thus the schema does the heavy lifting.

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 calculates Global Moran's I and returns specific scalar statistics. It distinguishes from sibling tools like hotspot_analysis by positioning it as a precursor to local analysis.

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 explicitly advises using the tool to test for clustering before choosing local hotspot or modeling workflows, providing clear context for when to use it versus alternatives.

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