ShowMeOnMap

Compute correlation between two layers

correlate_layers

Read-onlyIdempotent

Spatially join two existing workspace layers and return the Pearson correlation coefficient r, sample size n, two-sided p-value approximation, plus human-readable strength and direction labels. Pure read — no layer is added, no op is committed, no credit charge. Use this when you want a numeric answer without rendering; use add_fusion_layer when the user wants the visualization. Required: map_id, layer_a_id, layer_b_id, rationale.

Input Schema

TableJSON Schema

Name	Required	Description
`map_id`	Yes
`rationale`	Yes	Short audit-log label (≤300 chars) stating the user-facing goal this call serves, e.g. "add wildfire layer for the user's California query". Required on every call. Stored in the operations log so map edits stay traceable — we never see your chat history.
`layer_a_id`	Yes
`layer_b_id`	Yes
`value_field_a`	No
`value_field_b`	No

TDQS

A4.3/5.0

Behavior4/5

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

Annotations already declare readOnlyHint and idempotentHint. The description adds valuable context: 'no layer is added, no op is committed, no credit charge' and specifies the exact return values. No contradictions.

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 plus a required-fields note. Every sentence adds value: purpose, usage guidance, and required parameters. No filler.

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 no output schema, the description adequately describes return values and behavior. It lacks details on parameter semantics and edge cases (e.g., non-overlapping layers), but overall covers the main points.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is only 17%. The description mentions the required parameters (map_id, layer_a_id, layer_b_id, rationale) but does not explain their meaning. It omits any mention of the optional parameters value_field_a and value_field_b.

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 specifies the operation: spatially join two layers and return correlation statistics. It distinguishes from the sibling tool add_fusion_layer by stating when to use each (numeric answer vs visualization).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicit guidance on when to use this tool ('when you want a numeric answer without rendering') and when to use the alternative (add_fusion_layer for visualization). Also notes it's a pure read operation.

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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TDQS

A3.9/5.0

Disambiguation4/5

Most tools have clearly distinct purposes, but a few pairs such as select_within/focus_area and correlate_layers/add_fusion_layer overlap in function, differing mainly by input type or output (read vs. write). The descriptions adequately explain these differences, so agents can distinguish them with careful reading.

Naming Consistency5/5

Tool names consistently follow a verb_noun snake_case pattern (add_layer, filter_layer, export_image, remove_annotation). Minor exceptions like undo/redo/tag are conventional single verbs and do not detract from the overall predictability.

Tool Count4/5

At 25 tools, the server is on the heavy side, but each tool serves a distinct operation in a comprehensive mapping workspace—covering creation, editing, analysis, export, and history. The number feels justified by the broad feature set rather than excessive.

Completeness4/5

The toolset covers the full lifecycle of layers and workspace state, including add/remove/rename/restyle/filter, build/get/mutate/undo/redo, export, and tagging. Minor gaps such as annotation editing, layer reordering, or direct data updates exist, but they are not critical blockers for typical workflows.

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