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List Dataflows

list_dataflows
Read-onlyIdempotent

Browse or keyword-search ISTAT datasets (dataflows). Each result has an id (the dataflowId you pass to get_data / dataflow_structure) and an English name. ISTAT publishes ~4,800 datasets, so always pass query to filter unless you really want the whole list. Example: list_dataflows({ query: "unemployment" }) or list_dataflows({ query: "GDP" }).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax results to return (default 50).
queryNoCase-insensitive substring filter on dataset id or name, e.g. "population", "inflation", "GDP".

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "query": "unemployment"
+  },
+  {
+    "limit": 20,
+    "query": "GDP"
+  }
+]
  2. First observed

TDQS

A4.8/5.0
Behavior5/5

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

Annotations already provide readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false. The description adds useful context: 4800 datasets, result fields, and filtering behavior, complementing annotations without contradiction.

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 sentences with a clear purpose, details, and examples. No wasted words; effective front-loading.

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?

For a simple list tool with two optional parameters, the description explains the result structure (id and name) and filter behavior. Annotations cover safety. Complete for the task.

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?

Both parameters are fully described in the schema (100% coverage). The description adds value by explaining query as case-insensitive substring filter and providing usage examples, plus mentioning limit default.

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 it lists/browses ISTAT datasets (dataflows), specifies that each result has an id and English name, and implicitly distinguishes from siblings like get_data and dataflow_structure by mentioning the id's use.

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?

Explicitly advises to always pass the query parameter unless the full list is needed, with examples. Though it doesn't explicitly contrast with siblings, the mention of get_data/dataflow_structure provides context.

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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