ABS Data (observed)

Describe a table

describe_table

Read-onlyIdempotent

A table's dimensions in key order, its coverage dates, and its options. Dimensions with up to 64 options list them inline with labels; larger ones (geography, occupations) say how many and are searchable with search_options.

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": {
      "table": "<table>"
    },
    "name": "describe_table"
  }
}

Input Schema

Name	Required	Description	Default
`table`	Yes	Dataflow id, e.g. CPI

Output Schema

Name	Required	Description
`table`	Yes
`keyFormat`	Yes	Dimension ids joined by '.', the order a selection is serialised in
`dimensions`	Yes
`exampleUrl`	Yes
`provenance`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedSep 9, 2026

TDQS

A3.8/5.0

Behavior4/5

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

Annotations already declare the tool as read-only, idempotent, non-destructive, and closed-world. The description adds useful behavioral detail beyond those annotations: dimensions with up to 64 options are listed inline with labels, while larger ones are summarized by count and defer to search_options. That threshold behavior is the kind of context an agent needs to interpret the response correctly.

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?

Two compact sentences, front-loaded with what the tool returns and followed by the key behavioral rule for option visibility. Every sentence earns its place and there is no redundancy.

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?

The definition is complete enough for a read-only metadata tool: annotations cover safety, the output schema exists, the lone parameter is documented, and the description explains the inline-versus-counted option behavior. A little explicit guidance about when to use this versus search_tables would make it fully complete.

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 description coverage is 100%, and the single table parameter is documented in the schema as a dataflow id such as CPI. The description does not add any further meaning about the parameter, so the schema does the heavy lifting and the baseline of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states what the tool returns: a table's dimensions in key order, coverage dates, and options. It distinguishes itself from search_options by explaining that large dimensions are searchable there, but it never explicitly frames itself against search_tables or search, leaving some sibling differentiation implicit.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is implied rather than stated: the agent learns this is the way to inspect table metadata, and that large option lists should be explored via search_options. However, there is no explicit when-to-use guidance versus search_tables or search, nor any stated prerequisites for the table parameter.

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

All ABS Data (observed) tools Inspect

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