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Istat It

by io.github.pipeworx-io
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Get Data

get_data
Read-onlyIdempotent

Pull observations from an ISTAT dataset. key is a dot-separated SDMX dimension filter, one position per dimension in the order given by dataflow_structure; leave a position empty to wildcard it. Example: get_data({ dataflow_id: "101_1015", key: "A.IT...", start_period: "2021", end_period: "2023" }) selects annual (A), REF_AREA=IT (Italy), and wildcards the rest. Omit key (or pass "") to fetch all series — caution, this can be large. Returns decoded series with their dimension labels and per-period values.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
keyNoDot-separated dimension filter (one position per dimension, empty = wildcard), e.g. "A.IT..." . Omit for all series.
last_nNoReturn only the last N observations per series.
end_periodNoEnd period, e.g. "2023".
max_seriesNoCap the number of decoded series returned (default 200).
dataflow_idYesISTAT dataflow id, e.g. "101_1015".
start_periodNoStart period, e.g. "2021", "2021-01", "2021-Q1".

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "dataflow_id": "101_1015",
+    "end_period": "2023",
+    "key": "A.IT...",
+    "start_period": "2021"
+  },
+  {
+    "dataflow_id": "101_1015",
+    "key": "M.IT...",
+    "last_n": 12
+  }
+]
  2. First observed

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already indicate readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the tool is safe. The description adds significant context: the key filtering mechanism, wildcard behavior, example usage, and the return format (decoded series with labels and values). It also warns about large responses. No contradictions with annotations.

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?

The description is relatively concise, packing essential information into two sentences. It front-loads the purpose and then explains the key mechanism with an example. Minor improvement: could break into bullet points for clarity, but overall efficient.

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 6 parameters and no output schema, the description adequately covers the key behavior, usage caution, and return format. It does not fully explain every parameter (e.g., 'max_series', 'last_n'), but the schema provides descriptions. The description is sufficient for an agent to invoke the tool correctly.

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?

Schema coverage is 100%, providing a baseline of 3. However, the description enriches parameter meaning significantly. It explains that 'key' is a dot-separated filter with positions tied to dataflow_structure, how to wildcard, and that omitting it fetches all series. For 'start_period', it gives example formats. This goes well beyond the schema descriptions.

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 verb 'Pull' and the resource 'observations from an ISTAT dataset'. It distinguishes from sibling tools like 'dataflow_structure' and 'list_dataflows' by focusing on data retrieval. The specific mention of SDMX dimension filtering further sharpens the purpose.

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 clear context for usage, including a caution about fetching all series without a key. It implies when to use (to retrieve filtered observations) and when to be cautious (large results). However, it does not explicitly compare to alternatives like 'dataflow_structure' or 'list_dataflows'.

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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