Georgia Civic Data

List Tables for SQL Queries

link_tables

Read-onlyIdempotent

The tables link_query can read and the keys that join them: datasets to lookup tables to Census geography. With no arguments, a short index: each dataset's table name for SQL (e.g. 'education.attendance', its default detail level), what one row is (grain), detail levels, and join keys, plus each lookup table's name (e.g. 'dimensions.districts'). tables (names from the index, e.g. 'education.attendance', 'education/gosa/attendance', 'attendance', or 'districts') returns those tables in full: every column and a table name per detail level ('education.attendance__districts'). main_topic limits either form to one subject area.

Input Schema

Name	Required	Description	Default
`tables`	No	Tables to return in full. Omit for the short index.
`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.

Changed2 schema fields changedOct 26, 2026

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"
+}

addedInput schema / properties / tables / description

Added value: +"Tables to return in full. Omit for the short index."

First observedSep 7, 2026

TDQS

A4.7/5.0

Behavior5/5

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

Beyond simple metadata, the description provides crucial structural behaviors, such as defining what a single row is (`grain`) and detailing the naming convention for multi-level detail ('education.attendance__districts'). This highly specific information guides the AI on data representation.

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?

While dense, the structure is logical. It front-loads the context (link query function), defines the default behavior, and then explains the specialized behavior, maintaining a strong flow despite the technical complexity.

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?

Given the complexity of the output (two different result formats—index vs. full table—and naming conventions), the description is maximally complete, ensuring the agent understands all potential data states and how to filter them.

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

Parameters5/5

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

Despite the schema being 100% covered, the description adds immense semantic value. It explains the precise nature of the strings accepted by the 'tables' parameter, listing multiple expected formats (e.g., 'education.attendance', 'education/gosa/attendance'), which clarifies the input requirements far beyond the schema definition.

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 purpose is immediately clear: linking query tables and their join keys. It expertly differentiates the two modes of operation—providing a short index versus returning tables in full.

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 establishes a clear decision boundary: using no arguments leads to a short index, whereas providing the `tables` parameter triggers a full return of data. This implicitly guides usage logic based on the required output detail.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Georgia Civic Data tools Inspect

Try in Browser

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

Resources

Need Help?