Georgia Civic Data

List Datasets

list_datasets

Read-onlyIdempotent

The catalog: every published Georgia dataset and the shared lookup tables (district, school, county, census tract, and demographic names). By default each dataset is compact: its name (what every other tool takes), key (main_topic/sub_topic/topic), title, one-line summary, and first and last year. detail='full' adds detail levels, year gaps, whether it has a demographic breakdown, tags, and the contract version. main_topic ('education', 'census', 'immigration', 'health') limits the list to one subject area. The license object gives the terms the data is published under (CC BY 4.0). For a question about a known subject, search_datasets returns fewer, more relevant entries; describe_dataset gives one dataset's full schema.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "list_datasets"
  }
}

Input Schema

Name	Required	Description	Default
`detail`	No	'compact' (default) or 'full'.	compact
`main_topic`	No	Only datasets in this subject area. Omit for all.

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedNov 2, 2026

changedInput schema / properties / main_topic / enum

Previous value: -[
-  "census",
-  "education",
-  "immigration",
-  null
-]New value: +[
+  "census",
+  "education",
+  "health",
+  "immigration",
+  null
+]

Changed2 schema fields changedOct 26, 2026

addedInput schema / properties / detail

Added value: +{
+  "default": "compact",
+  "description": "'compact' (default) or 'full'.",
+  "enum": [
+    "compact",
+    "full"
+  ],
+  "title": "Detail",
+  "type": "string"
+}

addedInput schema / properties / main_topic

Added value: +{
+  "anyOf": [
+    {
+      "type": "string"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null,
+  "description": "Only datasets in this subject area. Omit for all.",
+  "enum": [
+    "census",
+    "education",
+    "immigration",
+    null
+  ],
+  "title": "Main Topic"
+}

First observedSep 7, 2026

TDQS

A4.6/5.0

Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, openWorldHint=false, and destructiveHint=false, so safety is covered. The description still adds real behavioral detail the annotations cannot: the exact shape of a compact row, what detail='full' adds (detail levels, year gaps, demographic breakdown, tags, contract version), and the license terms returned. It stops short of stating list size/pagination limits, which keeps it at 4.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with the catalog contents, then detail behavior, then filtering, then sibling routing — a sensible order with no filler sentences. It is on the long side for a two-parameter list tool, but nearly every clause carries information an agent would otherwise have to discover.

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?

With an output schema present, the description needn't restate return structure, and it still outlines what compact vs. full yield plus the license field. Combined with full schema coverage and rich annotations, nothing needed to invoke this tool correctly is missing.

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, but the description goes beyond the bare enums: it spells out the four main_topic values ('education', 'census', 'immigration', 'health') and explains what each detail mode returns rather than restating 'compact (default) or full'. It even clarifies that 'name' is the identifier every other tool accepts.

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?

States a concrete verb and resource ('every published Georgia dataset and the shared lookup tables') and enumerates the categories included, so the agent knows exactly what the listing contains. It also distinguishes itself from search_datasets ('returns fewer, more relevant entries') and describe_dataset ('one dataset's full schema').

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?

Explicit routing is given: for a known subject use search_datasets, for a single dataset's schema use describe_dataset, otherwise enumerate here. That is both a when-to-use and a when-not-to-use statement naming the alternatives.

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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