gamma_statistic

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

With no annotations and a minimal description, the behavioral transparency is poor. The description does not disclose side effects, computational requirements, or constraints like data size or coordinate system handling.

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 concise at one sentence, but it sacrifices critical information. While every word is earned, the brevity leads to omission of necessary context.

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 many siblings, the description is incomplete. An output schema exists but doesn't compensate for the lack of explanation about what the Gamma Statistic measures and how it differs from other autocorrelation tests.

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%, but the description adds no information about parameters. Keys like 'distance_threshold' and 'dependent_var' remain unexplained, forcing the agent to rely on names alone.

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 verb 'Compute' and resource 'Gamma Statistic' with context 'spatial autocorrelation', providing a basic purpose. However, it does not differentiate from sibling tools like morans_i or gearys_c, leaving ambiguity about when to use this specific statistic.

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 lacks any indication of when to use this tool versus alternatives, what prerequisites are needed (e.g., data type, variable assumptions), 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.