Georgia Civic Data

link_tables

List the tables link_query can read (curated gold paths only) and the join keys that bridge facts → dimensions → Census geography. Call this BEFORE writing a link_query. Two-tier to stay context-cheap: with NO arguments it returns a LEAN index — every table's name, grain, detail levels, default read_parquet(...) snippet, and join keys (enough to pick tables and write a single-detail join). To get every column and a snippet per detail level for the few tables you actually need, call again with tables=["<name>", ...] (a name from the index, e.g. 'education/gosa/attendance' or 'attendance', or a dimension like 'districts'). Paste the read_parquet(...) snippets verbatim into your SQL — they are exactly what the sandbox accepts.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`tables`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

First observedSep 7, 2026

TDQS

A5/5.0

Behavior5/5

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

No annotations are provided, so the description carries the full burden. It thoroughly discloses the two-tier behavior, the exact contents of each response level, and even the practical detail that read_parquet snippets must be pasted verbatim because the sandbox accepts them. This is strong behavioral transparency beyond the name and schema.

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?

Although the description is long, every sentence earns its place by adding operational detail: purpose, call timing, two-tier behavior, parameter examples, and usage instruction. It is front-loaded with the core purpose and structured logically, so the length is justified by the tool's richness.

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?

The description covers the tool's entire workflow, parameter semantics, output contents at both tiers, and integration with link_query. Since an output schema exists, return values need not be enumerated. The only possible missing detail would be error conditions, but they are not necessary for an agent to select and invoke this tool correctly.

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?

Schema description coverage is 0%, so the description must fully explain the tables parameter. It does: no arguments returns the lean index, supplying tables=['<name>', ...] returns full columns and snippets, and it provides concrete example values like 'education/gosa/attendance', 'attendance', or 'districts'. This fully compensates for the empty schema.

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 identifies the resource ('tables link_query can read'), the scope ('curated gold paths only'), and the output (join keys bridging facts → dimensions → Census geography). It distinguishes itself from sibling tools like list_datasets and link_query by specifying it is the metadata lookup to use with link_query.

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 gives an explicit directive: 'Call this BEFORE writing a link_query.' It further specifies a clear two-tier invocation pattern: first call with no arguments to get the lean index, then call again with tables to get detailed columns. This leaves no ambiguity about when and how to use the tool.

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

A4.5/5.0

Disambiguation5/5

Each tool maps to a distinct capability: discovery, schema, values, entity resolution, row query, aggregation, cross-dataset joins, and dimension reads. Even the pairs that sound closest (list_datasets vs search_datasets, describe_dataset vs get_contract) are explicitly differentiated by behavior and use case.

Naming Consistency4/5

Names are uniformly lowercase snake_case and mostly follow a clear verb_noun pattern (describe_dataset, query_dataset, resolve_entity, link_tables). Only aggregate (bare verb) and distinct_values (noun phrase without a verb) deviate slightly from that pattern.

Tool Count5/5

Twelve tools is a well-scoped size for a data-access MCP server. Each tool adds a non-redundant capability, supporting the full workflow from discovery and schema inspection to querying, aggregation, and cross-dataset joins.

Completeness5/5

The surface covers the complete read-only lifecycle: catalog discovery, schema/dimension inspection, value and entity lookup, row-level queries, grouped aggregation, cross-dataset analysis, and dimension table reads. Bulk data access is handled via pointers in query results rather than a dedicated tool, which is a deliberate non-gap for this query-focused server.

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