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Abs Au

by io.github.pipeworx-io
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Dataflow Structure

dataflow_structure
Read-onlyIdempotent

For one ABS dataflow, return its ordered dimensions and the valid codes for each. Use this to build a dataKey for get_data: the key has one dot-separated position per dimension, in the order returned here. Call this before get_data.

Example tool call

This call uses example arguments supplied in the tool’s input schema.

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

Input Schema

NameRequiredDescriptionDefault
dataflowIdYesDataflow id from list_dataflows, e.g. "CPI".
maxCodesPerDimensionNoCap codes listed per dimension to keep output small (default 50).

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "dataflowId": "CPI"
+  },
+  {
+    "dataflowId": "ABS_REGIONAL_LGA2021",
+    "maxCodesPerDimension": 30
+  }
+]
  2. First observed

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is known. The description adds valuable behavioral context about the ordered nature of dimensions and the significance of that order for constructing dataKey, which goes beyond the annotations.

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?

The description is two sentences, front-loaded with the core function, then immediately provides usage context. No filler or redundant information.

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?

For a simple tool with two parameters and no output schema, the description provides sufficient context: what it returns, how it relates to get_data, and the ordering requirement. Some detail on output structure could be added, but the essential information is present.

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 coverage is 100%, with both dataflowId and maxCodesPerDimension described in the schema. The description reinforces dataflowId's role but does not add significant meaning beyond the schema, so a baseline of 3 is appropriate.

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 states the tool's function: 'return its ordered dimensions and the valid codes for each' for one ABS dataflow. It also distinguishes itself from siblings by explicitly linking to get_data as a prerequisite, making its unique purpose evident.

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 provides explicit when-to-use guidance: 'Call this before get_data' and explains how to use the output to build a dataKey. It does not mention alternatives or when-not-to-use, but the directive is clear and actionable.

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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