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muend

arcgis-mcp-bridge

kernel_density

Estimate continuous density surfaces from point or polyline features to create hotspot, intensity, and service demand maps using kernel density interpolation.

Instructions

Estimate a continuous density raster from point or polyline features using Spatial Analyst KernelDensity. Use this for hotspot surfaces, incident intensity, service demand, accessibility pressure, crime or event density, and other smoothed spatial concentration maps. Requires a Spatial Analyst license; supports population_field, cell_size, and search_radius and writes out_raster inside PathGuard roots.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
paramsYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior3/5

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

With no annotations, the description must convey behavioral traits. It mentions the license requirement and that output writes inside PathGuard roots, but does not disclose error handling, temporary file creation, or performance implications. The description adds some context beyond the schema but is not fully transparent.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences, front-loading the main purpose. The list of use cases is slightly verbose but adds clarity. It could be trimmed without losing value, but overall it is efficient.

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 complexity of kernel density and the existence of an output schema, the description covers license constraints, input types, and allowed output paths. It misses prerequisites like Spatial Analyst extension activation, but is largely adequate for the tool's purpose.

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?

Context indicates schema description coverage is 0%, so the description should compensate. It lists supported parameters (population_field, cell_size, search_radius) and mentions out_raster, but does not explain their roles or behavior (e.g., how search_radius affects smoothness). The description adds minimal meaning beyond parameter names.

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?

Description clearly states the tool estimates a continuous density raster from point/polyline features using Spatial Analyst. It lists specific use cases like hotspot surfaces and crime density, which distinguishes it from sibling tools like contour_lines or hot spot analysis that have different purposes.

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?

Description provides clear use cases and notes the requirement for a Spatial Analyst license. However, it does not explicitly state when not to use this tool or mention alternatives among siblings, which would be beneficial for an agent deciding between kernel_density and similar tools like hotspot_analysis.

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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