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Glama

Arcgis Lorain

by io.github.pipeworx-io

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. Dates show when Glama detected each change.

  1. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "limit": 50,
+    "out_fields": "*",
+    "url": "https://services.arcgis.com/lorain/rest/services/Parcels/FeatureServer/0",
+    "where": "1=1"
+  },
+  {
+    "limit": 100,
+    "order_by": "VALUE DESC",
+    "out_fields": "PARCEL_ID,OWNER,VALUE",
+    "url": "https://services.arcgis.com/lorain/rest/services/PublicWorks/MapServer/2",
+    "where": "ZONE = 'Residential'"
+  }
+]
  2. First observed

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, but the description adds that it returns attribute rows and geometry, and describes SQL-like filtering behavior. The sampling tip and explicit mention of output format go beyond the annotations, providing useful behavioral context. No contradiction with 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?

Two sentences: first states purpose and key parameters, second gives a usage tip. No fluff, well front-loaded with the verb and resource. Every word earns its place.

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?

With no output schema, the description's mention of returning rows and geometry helps fill that gap. It also references the predecessor tool (search_datasets) and provides a sample query pattern. Overall, it is fairly complete for a query tool, though it could include pagination behavior notes beyond offset.

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

Parameters4/5

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

Schema coverage is 100%, so baseline is 3. The description adds value by explaining SQL-like semantics ('where', 'out_fields', 'order_by') and giving a practical sampling pattern. It enhances rather than repeats schema info, justifying a score above baseline.

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/Map Service layer by URL, using a specific verb ('Query') and resource. It distinguishes from sibling tools like layer_info (metadata) and search_datasets (dataset discovery) 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 indicates the URL comes from search_datasets, implying a workflow of first finding a dataset then querying it. The sampling tip ('Use where="1=1" + out_fields="*"') provides concrete usage context. However, it does not explicitly exclude alternatives or state when not to use, leaving some ambiguity.

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

A3.8/5.0
Disambiguation3/5

Tools are diverse across GIS, brand audits, prediction markets, and subscription management. While each tool has detailed descriptions, the broad domain mix confuses which tool to use for a given task. Some overlap exists among Pipeworx tools (e.g., ask_pipeworx, ask_pipeworx_grounded, deep_research), making disambiguation moderate.

Naming Consistency3/5

Tool names follow snake_case but verbs vary (e.g., query_layer vs. ask_pipeworx vs. generate_llms_txt). Subgroups like 'polymarket_*' are consistent, but the overall set lacks a uniform naming pattern, reducing coherence.

Tool Count2/5

33 tools is excessive for a server named after Lorain County GIS. Only three tools (layer_info, query_layer, search_datasets) relate to that purpose; the rest are unrelated APIs. The tool count is mismatched to the server's implied scope.

Completeness2/5

For a GIS server, the tool surface is incomplete—missing editing, upload, and administrative tools. Moreover, the inclusion of many irrelevant tools (e.g., prediction markets, npm package checks) dilutes focus and leaves the core domain under-served.

Resources