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Arcgis Kingston

by io.github.pipeworx-io
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Query Layer

query_layer
Read-onlyIdempotent

Query an ArcGIS Feature Service / Map Service layer by its url (from search_datasets). SQL-like where, comma-separated out_fields, order_by, limit, offset. Returns attribute rows (and geometry). Use where="1=1" + out_fields="*" to sample.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesFeature/Map Service layer url ending in /FeatureServer/<n> or /MapServer/<n>.
limitNoMax features (1-2000, default 50).
whereNoSQL where clause, e.g. "STATE = 'CA' AND YEAR >= 2020". Default "1=1".
offsetNoPagination offset.
order_byNoe.g. "POP DESC".
out_fieldsNoComma-separated field names, or "*" for all (default).

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "limit": 50,
+    "out_fields": "*",
+    "url": "https://services.arcgisonline.com/arcgis/rest/services/Kingston/FeatureServer/0",
+    "where": "1=1"
+  },
+  {
+    "limit": 100,
+    "order_by": "ADDRESS ASC",
+    "out_fields": "PARCEL_ID,ADDRESS,ZONE",
+    "url": "https://services.arcgisonline.com/arcgis/rest/services/Kingston/FeatureServer/1",
+    "where": "ZONE = 'Residential'"
+  }
+]
  2. First observed

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already establish the read-only, safe nature, so the description's added value is the return format ('Returns attribute rows (and geometry)') and the sampling hint. It doesn't contradict annotations and adds useful behavioral context, though it could disclose default result size or pagination behavior (though schema covers defaults).

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?

The description is two sentences, with the main verb and resource up front. It efficiently summarizes the parameters and ends with a practical tip, with zero 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?

For a read-only query tool with a schema covering all parameters, the description covers the essential context: source of URL, query capabilities, return type, and a usage tip. It could explicitly mention default limit max and pagination, but these are in the schema, so the description is adequate for the tool's complexity.

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?

Schema descriptions cover 100% of parameters with concrete examples and defaults, so the description's role is minimal. It restates parameter names and notes SQL-like 'where' and comma-separated 'out_fields', which adds a little conceptual framing but little new meaning beyond the schema.

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 identifies the tool's function: querying ArcGIS Feature/Map Service layers via URL. It distinguishes itself by referencing search_datasets as the source, separating it from sibling tools like layer_info or search_within. The listed parameters further clarify the operation scope.

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 implies a workflow: first use search_datasets to find a layer URL, then query with this tool. It also provides a practical sampling technique (where="1=1" + out_fields="*"). However, it does not explicitly state when to use this versus alternatives like search_within or layer_info, so it lacks explicit exclusion guidance.

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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