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by io.github.pipeworx-io
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Dataflow Structure

dataflow_structure
Read-onlyIdempotent

Get the structure (Data Structure Definition) of one ISTAT dataset: its ordered dimensions and, for each, the valid codes. Use this to learn how to build the dot-separated SDMX key for get_data. The key positions correspond to the dimensions in order; an empty position is a wildcard. Example: dataflow_structure({ dataflow_id: "101_1015" }).

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": {
      "dataflow_id": "101_1015"
    },
    "name": "dataflow_structure"
  }
}

Input Schema

NameRequiredDescriptionDefault
dataflow_idYesISTAT dataflow id from list_dataflows, e.g. "101_1015".

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "dataflow_id": "101_1015"
+  }
+]
  2. First observed

TDQS

A4/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds that empty positions in keys are wildcards, which is useful but not contradictory. It provides context on the output's purpose beyond what annotations indicate.

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 three sentences including an example, no redundant words, and is front-loaded with the main action.

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?

Given the tool's simplicity (one parameter, no output schema, safety annotations), the description covers the output and its use case well. Minor omission: no mention of error handling for invalid dataflow IDs, but not critical.

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 the parameter already well described in the schema. The description reiterates the same info with an example but adds no new semantics. Baseline 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 specifies the verb ('Get the structure') and resource ('ISTAT dataset'), and explains how the result (ordered dimensions and codes) is used to build SDMX keys for get_data. It distinguishes itself from siblings like get_data and list_dataflows.

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 explicitly states when to use this tool: to learn how to build the SDMX key for get_data. It provides an example. While it doesn't state when not to use it, the context is clear enough.

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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