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package_release_tool

Scan a dataset or model directory, redact it, and generate publication-ready Hugging Face cards and EU AI Act training-data summaries in one call.

Instructions

Scan a dataset/model directory, redact it, and generate the paperwork needed to publish it, in one call.

This is the end-to-end tool: use it when the goal is "make this directory publishable" rather than just inspecting or redacting it. It chains a Presidio scan, an optional redaction pass, and generation of a Hugging Face dataset/model card plus an EU AI Act Art. 53(1)(d) training-data-summary template, all populated from the same scan results so the documents and the redacted copy can never disagree. Prefer scan_directory_tool alone for a read-only report, or redact_directory_tool alone when you don't need the generated cards. This tool does not detect PII itself -- detection is entirely Presidio's, unmodified.

path must exist and be readable. This tool is mutating: it writes the bundle to output (dataset/model card, EU AI Act summary) and, when redact_first=True (the default), also writes a redacted copy to <output>-redacted-source, overwriting that directory if it already exists. Nothing under path itself is ever modified. No network calls are made -- scanning, redaction, and document generation all run locally. The EU AI Act summary is a draft template with scan-derived counts filled in and everything else left as an explicit placeholder for a human to complete; it is not a compliance guarantee.

kind is "dataset" (default), "model", or "both", and picks which card template(s) get generated. redact_first toggles the redaction step (default True); strategy is "mask", "hash", or "remove" and only applies when redact_first=True. Example calls: package_release_tool(path="./data", output="./release"), package_release_tool(path="./model", output="./release", kind="model"), package_release_tool(path="./data", output="./release", redact_first=False).

Returns a JSON object with bundle_dir, dataset_card_path (or null if kind="model"), model_card_path (or null if kind="dataset"), eu_ai_act_summary_path, and source_kind. On a missing path or any internal failure it returns {"error": ..., "error_type": ...} instead of raising. See releaseguard package --help for the CLI-equivalent flag reference.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
kindNodataset
pathYes
outputYes
strategyNomask
redact_firstNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

With no annotations provided, the description carries the full burden and excels. It discloses mutating behavior (writes to output, overwrites redacted source), guarantees nothing under the original path is modified, notes local execution/no network calls, admits the tool doesn't detect PII itself, and explicitly states the EU AI Act summary is a draft template, not a compliance guarantee. Error handling is also described. This is comprehensive behavioral disclosure.

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?

Though long, the description is well-structured and every sentence earns its place. It starts with a concise summary, then covers usage guidance, behavioral details, parameter semantics, return format, and error behavior in clear paragraphs. The inclusion of example calls is practical and not redundant. No fluff or repetition.

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?

Given the tool has 5 parameters, 0% schema coverage, no annotations, and a non-trivial side-effect profile, the description is remarkably complete. It explains all parameters, return values, error behavior, side effects, limitations, and even points to a CLI reference. The output schema may exist but the description still provides a clear explanation of the JSON return shape, making it self-contained.

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 description coverage is 0%, so the description must compensate, and it does fully. It explains the meaning of `path`, `output`, `kind` (with allowed values), `redact_first`, and `strategy` (with allowed values and applicability condition). It also provides three example invocations that clarify parameter combinations. All five parameters are meaningfully described.

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 opens with a specific verb+resource statement: 'Scan a dataset/model directory, redact it, and generate the paperwork needed to publish it, in one call.' It immediately differentiates from siblings by naming exact alternatives: 'Prefer `scan_directory_tool` alone for a read-only report, or `redact_directory_tool` alone when you don't need the generated cards.' This fully clarifies what the tool does and how it is distinct.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicit usage guidance is provided: 'use it when the goal is "make this directory publishable" rather than just inspecting or redacting it.' It also gives concrete conditions for alternatives and even includes example calls to illustrate common use cases. This goes beyond simple context and provides clear decision criteria.

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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