getis_ord_g_local

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

No annotations were provided, and the description gives no information about behavioral traits such as computational demands, data requirements, or side effects. The agent has no insight into what happens during execution.

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?

At three words, the description is extremely concise but at the expense of substance. It does not use its limited space to convey actionable information; instead, it sacrifices clarity for brevity.

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 local spatial statistic with multiple parameters and an output schema, the description provides virtually no context. It does not mention what the output represents, how distance_threshold influences results, or any interpretation guidelines.

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 0% description coverage, and the description does not explain any parameters. While parameter names like 'shapefile_path' and 'distance_threshold' are somewhat self-explanatory, the description adds no additional meaning, leaving the agent to guess default behaviors or constraints.

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 'Local Getis-Ord G' identifies the tool as computing the local Getis-Ord G statistic, which differentiates it from the sibling global version 'getis_ord_g'. However, it lacks explanation of what the statistic is used for (e.g., hot/cold spot detection), leaving some ambiguity for an AI agent unfamiliar with the term.

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?

No usage guidelines are provided. The description does not indicate when to use this tool over alternatives like 'moran_local' or 'getis_ord_g', nor does it clarify prerequisites or typical use cases.

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