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Glama

Arcgis Charlotte

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.arcgisonline.com/arcgis/rest/services/Charlotte/Parcels/FeatureServer/0",
+    "where": "1=1"
+  },
+  {
+    "limit": 100,
+    "order_by": "ACRES DESC",
+    "out_fields": "PARCEL_ID,ZONE_TYPE,ACRES",
+    "url": "https://services.arcgisonline.com/arcgis/rest/services/Charlotte/Zoning/FeatureServer/0",
+    "where": "ZONE_TYPE = 'Residential'"
+  }
+]
  2. First observed

TDQS

A4/5.0
Behavior3/5

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

Annotations already cover read-only and idempotence, so the description only needs to add extra context. It adds that the tool 'Returns attribute rows (and geometry)' and suggests a sampling pattern. This is useful, but it does not disclose any further behavioral traits like error handling or pagination edge cases. A 3 is appropriate given the annotation coverage.

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 plus a practical tip. It front-loads the core purpose and lists all relevant parameters without redundancy. Every sentence adds value, and it is appropriately concise.

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 schema covering all parameters and annotations covering safety, the description provides the essential return-type information and a usage hint. It does not fully elaborate on potential pagination behavior or error cases, leaving a minor gap, but it is sufficiently complete for a query tool with rich structured metadata.

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 coverage is 100% and each parameter has a description. The tool description summarizes the parameters (e.g., 'comma-separated out_fields') but adds little beyond the schema. The sampling tip is more of a usage guideline than parameter semantics. Thus baseline 3 is warranted.

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 states a specific verb and resource: 'Query an ArcGIS Feature Service / Map Service layer by its url'. It also lists the key query parameters and clarifies the return type, distinguishing it from sibling tools like layer_info (metadata) and search_datasets (dataset discovery).

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 provides clear context by mentioning the url comes 'from search_datasets', implying a workflow. The tip 'Use where="1=1" + out_fields="*" to sample' gives practical usage guidance. However, it does not explicitly contrast with alternatives like layer_info or search_within, so it stops short of a full when-not-to-use explanation.

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
Disambiguation2/5

Several tools are easy to confuse: ask_pipeworx_beta is currently identical to ask_pipeworx, deep_research overlaps heavily with ask_pipeworx/ask_pipeworx_grounded, and the six polymarket_* tools have closely related purposes. ai_visibility_check and scan_competitor_ai_presence also overlap, making selection error-prone.

Naming Consistency4/5

Almost all tools use lowercase snake_case and mostly follow a verb_noun pattern (query_layer, resolve_entity, scan_dependency, validate_claim). A few noun-style names like entity_profile, layer_info, and pipeworx_trending deviate slightly, but the overall convention is predictable.

Tool Count2/5

34 tools is well past the heavy threshold, and the server is named 'Arcgis Charlotte' while only three tools actually relate to ArcGIS. The rest form a sprawling Pipeworx research, prediction-market, subscription, and memory toolkit, which creates a severe scope mismatch.

Completeness2/5

For the Pipeworx data side the surface is quite thorough, but for the declared ArcGIS Charlotte purpose it is thin: search_datasets, layer_info, and query_layer provide read-only access with no update/delete, analysis, or dataset management. The tool set therefore has a significant gap relative to its stated domain.

Resources