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Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault

No arguments

Instructions

Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.

This server publishes no instructions, or was last inspected before Glama recorded them.

Capabilities

Features and capabilities supported by this server

Protocol revision2025-11-25

CapabilityDetails
tools
{
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prompts
{
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resources
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experimental
{}

Tools

Functions exposed to the LLM to take actions

NameDescription
scan_directory_toolA

Recursively scan a local dataset or model directory for PII and secrets before you publish it.

Call this before releasing, uploading, or sharing a dataset/model directory whenever you need to know what personal data it contains -- it is the read-only first step agents should take ahead of redact_directory_tool or package_release_tool, and the right choice on its own when you only need a report, not a redacted copy. Do not call it on directories you do not have read access to, or expect it to catch anything beyond what Presidio's own recognizers detect (no custom regex or heuristics are layered on top).

path must be a directory (or single file) that already exists on disk and is readable by the current process; it walks CSV, JSON/ JSONL, and plain-text files under it. This call is read-only: it never writes, moves, or deletes anything, makes no network requests (Presidio and spaCy run entirely locally), and is safe to call repeatedly -- re-running it against an unchanged directory returns the same findings. On a missing path, or any internal failure, it returns {"error": ..., "error_type": ...} instead of raising or crashing the server -- check for an error key before reading findings.

spacy_model selects the spaCy model Presidio's NLP engine uses (defaults to en_core_web_sm; must already be installed via python -m spacy download <model>, this tool does not install one). score_threshold (0.0-1.0, default 0.35) drops any finding below that Presidio confidence score -- raise it to cut false positives, lower it to widen recall. Example calls: scan_directory_tool(path="./data"), scan_directory_tool(path="./data", score_threshold=0.5), scan_directory_tool(path="./models/card-dir", spacy_model="en_core_web_lg").

Returns a JSON object with root_path, files_scanned, files_skipped, findings (a list of objects each with file_path, entity_type, start/end offsets, score, text_preview, line_number, field_name, detector), entity_counts (per-type totals), total_findings, detector_name, and language. For flag-level detail beyond this docstring, run the equivalent CLI form: releaseguard scan --help.

redact_directory_toolA

Scan a directory for PII/secrets with Presidio, then write a redacted copy to a new location.

Call this once you already know (or expect) a directory contains PII and want a sanitized copy you can hand off or publish, without touching the original. It runs its own internal scan first (same detector as scan_directory_tool), so you do not need to call scan_directory_tool beforehand unless you want to inspect findings before deciding to redact. Skip it if you only need a report (use scan_directory_tool) or if you also want the Hugging Face card and EU AI Act summary generated (use package_release_tool, which redacts as one step of a larger bundle).

path must exist and be readable. This tool is mutating but scoped to output only: it never edits, moves, or deletes anything under path. Writing fails if output already exists and is non-empty, unless overwrite=True -- pass that deliberately, since it will silently overwrite prior contents of output. No network calls are made; everything runs locally. Not idempotent across repeated calls with overwrite=True if the source directory changed between runs (the redacted copy reflects whatever path contains at call time).

strategy controls how each finding is replaced: "mask" (default, replaces matched text with a placeholder like <EMAIL_ADDRESS>), "hash" (replaces with a deterministic hash of the original value), or "remove" (deletes the matched span entirely). Example calls: redact_directory_tool(path="./data", output="./data-redacted"), redact_directory_tool(path="./data", output="./data-redacted", strategy="hash"), redact_directory_tool(path="./data", output="./data-redacted", overwrite=True).

Returns a JSON object with source_root, output_root, strategy, files_written (list of paths under output), entities_redacted (per-entity-type counts), and total_redacted. On a missing path, an invalid strategy, or a non-empty output without overwrite=True, it returns {"error": ..., "error_type": ...} instead of raising. See releaseguard redact --help for the CLI-equivalent flag reference.

package_release_toolA

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.

Prompts

Interactive templates invoked by user choice

NameDescription

No prompts

Resources

Contextual data attached and managed by the client

NameDescription

No resources

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