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Search Datasets

search_datasets
Read-onlyIdempotent

Search St. Louis County GIS open geospatial datasets (parcels, addresses, zoning & public works) 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 (St. Louis County 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"
      +  }
      +]
  2. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the tool is clearly non-destructive. The description adds beyond that by detailing the return structure (name, summary, record_count, owner/org, url) and the recommended follow-up usage, providing useful behavioral context for an agent.

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?

Single, well-structured sentence that front-loads the action and scope, includes key details about returns and chaining, and avoids fluff. Every part adds value.

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?

For a search tool with 3 parameters, no output schema, and rich annotations, the description fully captures purpose, return fields, and integration with siblings. Nothing essential is missing for an agent to use it correctly.

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% with adequate descriptions for all three parameters (limit, query, org_id). The description does not add details about the parameters beyond listing examples in the schema; it focuses on the output. Baseline 3 is appropriate as the schema does the heavy lifting.

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 uses precise verbs ('Search') and specifies the resource ('St. Louis County GIS open geospatial datasets') and scope ('by keyword'). It lists exact return fields and mentions how the output URL can be used with sibling tools (query_layer, layer_info), distinguishing it from those tools.

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?

The description implicitly states when to use the tool (when searching for datasets) and provides explicit guidance on how to chain it with query_layer/layer_info using the returned url. However, it does not explicitly describe when not to use it or compare it to other search-related siblings like search_within.

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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