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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.arcgis.com/sharing/rest/content/items/abc123/data/FeatureServer/0",
+    "where": "1=1"
+  },
+  {
+    "limit": 100,
+    "offset": 0,
+    "order_by": "POPULATION DESC",
+    "out_fields": "OBJECTID,NAME,POPULATION",
+    "url": "https://services.arcgis.com/sharing/rest/content/items/def456/data/FeatureServer/1",
+    "where": "STATE = 'CA' AND YEAR >= 2020"
+  }
+]
  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 declare readOnly, idempotent, and non-destructive behavior. The description adds value by specifying the return type ('attribute rows (and geometry)') and the SQL-like query semantics, which goes beyond the structured data.

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: the first states purpose and key parameters, the second gives a practical tip. Every word contributes, with no redundancy or fluff.

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 query tool with 6 parameters, it covers purpose, parameter list, return type, and a usage pattern. It does not mention authentication or rate limits, but the readOnlyHint annotation and the absence of an output schema reduce the burden. It is complete enough for straightforward use.

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 schema documents all parameters with details and examples, so baseline is 3. The description adds a useful tip for sampling (where="1=1" + out_fields="*") and classifies the where clause as SQL-like, but these are marginal enhancements over 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 states the action ('Query'), the resource ('ArcGIS Feature Service / Map Service layer'), and the prerequisite source of the URL ('from search_datasets'). This differentiates it from sibling tools like search_datasets (finding layers) and layer_info (metadata).

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?

It explicitly mentions that the URL comes from search_datasets, establishing a clear workflow. It also provides a concrete sampling pattern. It does not explicitly state when not to use it or mention alternatives, but the context is sufficient for an agent.

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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