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Arcgis Cedarpark

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/CedarPark/FeatureServer/0",
+    "where": "1=1"
+  },
+  {
+    "limit": 100,
+    "order_by": "ACREAGE DESC",
+    "out_fields": "PARCEL_ID,OWNER,ACREAGE",
+    "url": "https://services.arcgisonline.com/arcgis/rest/services/CedarPark/FeatureServer/2",
+    "where": "ZONING = 'Commercial'"
+  }
+]
  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 it read-only and idempotent; the description adds behavior details like returning attribute rows and geometry, and the SQL-like query interface. No contradiction found, and the added context is meaningful beyond annotations.

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 concise sentences, front-loaded with the action and resource. It avoids repetition, and every clause adds practical information including a concrete usage example.

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 moderate complexity, no output schema, and strong annotations/schema, the description covers core usage, return type, and important parameter behaviors. It lacks explicit pagination guidance and output format details, but remains sufficient for correct invocation.

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 has 100% coverage of all 6 parameters, so the baseline is 3. The description reinforces SQL-like behavior and comma-separated out_fields, but adds limited new semantic value beyond the rich schema descriptions.

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 queries an ArcGIS Feature Service / Map Service layer by URL, using a specific verb and resource. It also distinguishes itself from siblings like search_datasets and layer_info by focusing on data retrieval.

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 indicates the URL should come from search_datasets and provides a practical sampling tip ('where="1=1" + out_fields="*"'). It does not explicitly state when not to use it or mention alternatives, but context is clear.

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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