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Arcgis Fontana

by io.github.pipeworx-io
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Search Datasets

search_datasets
Read-onlyIdempotent

Search City of Fontana GIS open geospatial datasets (parcels, zoning, public works & city services) by keyword. Returns each dataset's name, summary, record_count, owner/org, and its Feature Service url — pass that url to query_layer / layer_info.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax datasets (1-50, default 20).
queryNoKeyword(s), e.g. "parcels", "crime", "flood zones".
org_idNoOptional ArcGIS orgId to override the default (City of Fontana GIS).

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "query": "parcels"
+  },
+  {
+    "limit": 10,
+    "query": "zoning flood"
+  }
+]
  2. First observed

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnly, openWorld, idempotent, non-destructive. The description adds the output contract: returns name, summary, record_count, owner/org, and url, plus the link to follow-up tools. It doesn't mention rate limits or errors, but annotations lower the bar.

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: one for purpose, one for output and next steps. No redundant information, fully front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite no output schema, the description specifies all returned fields, so the agent knows what to expect. It also explains how to chain with query_layer/layer_info. Combined with rich annotations and complete schema, the tool is fully understood.

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 (limit, query, org_id). The description reinforces 'by keyword' but doesn't add new meaning beyond the schema's detailed parameter descriptions, so baseline 3 applies.

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 searches City of Fontana GIS open geospatial datasets by keyword, explicitly listing example categories and the return payload. This distinguishes it from siblings like query_layer and layer_info, which operate on a specific dataset URL.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description instructs to pass the returned Feature Service `url` to query_layer or layer_info, effectively telling the agent when to use this tool (to find datasets) and what to do next. It implies that if you already have a URL, you should skip this and go to the downstream tools.

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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