morans_i

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

With no annotations available, the description carries full responsibility for behavioral transparency. It does not disclose how the statistic is computed, handling of weights or missing data, significance testing, or any side effects, providing almost no behavioral insight.

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 very concise at one sentence, starting with the action verb. However, it is overly terse, omitting essential details, so it is not a model of effective conciseness as it sacrifices completeness.

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 (spatial autocorrelation), 4 parameters with no schema descriptions, and an output schema whose content is unknown, the description provides no contextual completeness about input formats, output structure, or statistical interpretation.

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 tool description provides no explanations for any of the 4 parameters (shapefile_path, dependent_var, target_crs, distance_threshold). Users are left uninformed about parameter meaning, format, 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 clearly states the tool computes 'Moran's I Global Autocorrelation Statistic', identifying the specific statistical method. However, it does not differentiate this from sibling tools like 'gearys_c' or 'getis_ord_g' which also compute global autocorrelation statistics, limiting clarity for selection.

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 guidance is provided; there is no indication of when to use this tool versus alternatives, prerequisites, or exclusions. The description merely states what it does without contextualizing its application.

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