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Halpph

istat-mcp-server

by Halpph

get_data

Retrieve data from Italian statistical datasets by applying filters. Returns a download link for large or time-consuming requests.

Instructions

Get data from a dataset with filters. Attempt to retrieve data, if it times out or is too big,
return the URL of the file to download.

Args:
    dataflow_identifier: The identifier of the dataset.
    filters: A dictionary of filters to apply to the dataset.

Example:
    get_data(dataflow_identifier="139_176", filters={"freq": "M", "tipo_dato": ["ISAV", "ESAV"], "paese_partner": "WORLD"})

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataflow_identifierYes
filtersYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

No annotations provided, so description bears full burden. It discloses that the tool attempts retrieval and may return a download URL on timeout or large result, but does not mention rate limits, authentication, or data freshness. Adequate but not comprehensive.

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 concise with a clear function statement, argument descriptions, and an example. No redundant information, but the Args section partially duplicates schema titles.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity (2 params, nested object, output schema exists), the description covers core behavior and fallback. It lacks details on error handling, filter syntax, or output schema structure. Acceptable but not fully comprehensive.

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 0%, so the description must add value. It explains that dataflow_identifier is the dataset identifier and filters is a dictionary, and provides an example. This adds some meaning beyond the schema's titles, but does not fully detail constraints or formats.

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 purpose: 'Get data from a dataset with filters.' It also specifies fallback behavior (return URL if too big/times out), which distinguishes it from sibling tools like download_dataset or get_data_limited.

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?

The description implies usage for filtered data retrieval but does not explicitly state when to use this tool versus alternatives like get_data_limited. It lacks clear 'when-not-to-use' guidance or differentiation from siblings.

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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