healthcare-data

get_data_dictionary

Column-level data dictionary for one Dataplex product: tables, columns with types and descriptions, temporal coverage, and validated SQL including the non-obvious query patterns (current-release filters, dedupe flags, text-encoded numbers). Pass table to get one table's columns; enough to plan an analysis before subscribing.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "slug": "<slug>"
    },
    "name": "get_data_dictionary"
  }
}

Input Schema

Name	Required	Description	Default
`slug`	Yes
`table`	No	Optional table or view name, e.g. HOSPITAL_GENERAL_INFORMATION

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedNov 4, 2026

addedInput schema / properties / table

Added value: +{
+  "description": "Optional table or view name, e.g. HOSPITAL_GENERAL_INFORMATION",
+  "type": "string"
+}

First observedJul 31, 2026

TDQS

A4.1/5.0

Behavior3/5

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

No annotations are supplied, so the description carries the full burden. It discloses the returned subject matter and warns that the SQL encodes non-obvious patterns (current-release filters, dedupe flags, text-encoded numbers), which is useful context, but it says nothing about output size, whether the whole dictionary or pagination applies, cost, or any access restrictions.

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?

Two dense sentences, front-loaded with what the tool returns and followed by the parameter-narrowing behavior. The mid-sentence parenthetical list is heavy but each item carries information; there is no filler or restatement of the name.

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

Completeness4/5

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

With no output schema and no annotations, the description must explain the return payload, and it does so by enumerating tables, columns with types and descriptions, temporal coverage, and SQL. Remaining gaps are modest - the expected slug format and the rough size/scope of the payload are unstated.

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 only 50%: the optional 'table' param is documented in the schema, but 'slug' has no schema description. The description compensates by framing slug as identifying 'one Dataplex product' and by spelling out that passing 'table' narrows the result to a single table's columns - meaning that is not present in the structured 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?

States a specific artifact (a column-level data dictionary) scoped to 'one Dataplex product' and enumerates its contents: tables, column types/descriptions, temporal coverage, and validated SQL. That makes it clearly distinct from siblings such as get_product_details (product metadata) and get_sample_data (rows).

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?

Gives a concrete usage context ('enough to plan an analysis before subscribing') and a narrowing rule ('Pass table to get one table's columns'). It stops short of explicitly excluding alternatives like get_sample_data or stating when a caller should skip this tool, so it is clear but not complete routing guidance.

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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