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Halpph

istat-mcp-server

by Halpph

download_dataset

Download a dataset from a URL to a local file with automatic format detection. Use for large datasets that cannot be handled in memory.

Instructions

Download a dataset file from a URL to a local path with automatic format detection.
Better for large datasets that cannot be handled in-memory or when Json responses are too large or not supported.
The file extension is automatically determined from the Content-Type header.

Args:
    url: The URL of the file to download.
    output_path: Optional. A relative or absolute path for the saved file.
                 If relative, it's resolved against the configured storage directory.
                 If absolute, it MUST be inside the storage directory.
                 If not provided, a filename is generated from the URL with the appropriate extension.

Example:
    # Saves to <storage_dir>/my_data/export.xml (extension based on content type)
    download_dataset(url="http://.../data", output_path="my_data/export")

    # Saves to <storage_dir>/<generated_name>.<ext> (extension based on content type)
    download_dataset(url="http://.../data")

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYes
output_pathNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

Discloses automatic format detection from Content-Type header and output path constraints (relative vs absolute, storage directory). No annotations provided, but description covers key behavioral aspects for a download tool.

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?

Very concise, structured with purpose, usage guidance, args, and example. Every sentence adds value, no redundancy.

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 presence of an output schema (not shown) and simple parameters, the description covers usage well. Could mention overwrite behavior, but overall sufficiently complete for effective tool usage.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, but the description adds full semantic meaning: url is the source, output_path is optional with relative/absolute resolution and default filename generation. Compensates well for the lack of 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 tool downloads a dataset file from a URL to a local path with automatic format detection, and distinguishes itself from sibling tools by highlighting it's better for large datasets or when JSON responses are too large.

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?

Provides explicit guidance on when to use this tool (large datasets, JSON too large) and an example. Does not explicitly state when not to use, but context is clear enough for the agent to decide.

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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