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arcgis-mcp-bridge

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Python
Local

spatial_autocorrelation

Evaluate whether a numeric attribute is clustered, dispersed, or random using Global Moran's I. Returns key statistics for spatial modeling decisions.

Instructions

Calculate Global Moran's I using ArcPy SpatialAutocorrelation and return scalar statistics including Moran's Index, z-score, and p-value. Use this to test whether a numeric attribute is clustered, dispersed, or spatially random before choosing local hotspot, cluster, or spatial modeling workflows. Reads in_features only and does not create or modify datasets.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
paramsYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed7 schema fields changedv0.6.3
    • changedInput schema / $defs / SpatialAutocorrelationInput / description
      Previous value: -"Global Moran's I — returns scalar statistics, writes no dataset."New value: +"Input contract for Global Moran's I, returning scalar statistics only."
    • addedInput schema / $defs / SpatialAutocorrelationInput / properties / conceptualization / description
      Added value: +"Spatial relationship model used to define feature neighbors, such as INVERSE_DISTANCE, FIXED_DISTANCE_BAND, K_NEAREST_NEIGHBORS, or contiguity-based relationships."
    • addedInput schema / $defs / SpatialAutocorrelationInput / properties / distance_band / description
      Added value: +"Optional distance threshold in map units used for distance-based spatial relationships. Use None when no explicit threshold is needed."
    • addedInput schema / $defs / SpatialAutocorrelationInput / properties / distance_method / description
      Added value: +"Distance calculation method used for spatial weights. Use EUCLIDEAN_DISTANCE for straight-line distance or MANHATTAN_DISTANCE for grid-like movement."
    • addedInput schema / $defs / SpatialAutocorrelationInput / properties / in_features / description
      Added value: +"Absolute path to the input feature class whose spatial autocorrelation will be measured. The path must be inside a configured PathGuard allowed root."
    • addedInput schema / $defs / SpatialAutocorrelationInput / properties / input_field / description
      Added value: +"Numeric attribute field used to calculate Global Moran's I spatial autocorrelation statistics."
    • addedInput schema / $defs / SpatialAutocorrelationInput / properties / standardization / description
      Added value: +"Spatial weights standardization method. ROW standardizes neighbor weights by row; NONE uses raw weights."
  2. First observedv1.0.0

TDQS

A4.2/5.0
Behavior4/5

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.

Conciseness5/5

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.

Completeness4/5

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.

Parameters3/5

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.

Purpose5/5

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.

Usage Guidelines4/5

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.

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