Spec Driven Development
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
제작자가 쓴 설명(영어 원문)
Scans datasets/models for PII/secrets, redacts, and packages a public-release bundle via MCP.
이 세 가지는 절대 하나의 점수로 합치지 않아요. 의견, 측정, 인기는 서로 다른 것이니까요.
이 서버는 아직 쉬운 말로 된 요약을 쓰지 못했어요. 아래는 레지스트리에서 읽어 온 사실이에요.
쓰고 있는 AI 앱을 고른 다음 설정을 복사하세요.
필요한 것
어떤 앱을 쓰세요?
위에서 앱을 고르세요. 기억해 두었다가 다음부터는 버튼에 “…에 설치”라고 표시해요.
Claude Desktop → Settings → Developer → Edit Config를 열고 claude_desktop_config.json에 이 내용을 붙여 넣으세요
{
"mcpServers": {
"releaseguard": {
"command": "uvx",
"args": [
"releaseguard-cli",
"releaseguard",
"mcp"
]
}
}
}터미널에서 이 명령어를 실행하세요
claude mcp add releaseguard -- uvx releaseguard-cli releaseguard mcp프로젝트의 .cursor/mcp.json 또는 ~/.cursor/mcp.json에 붙여 넣으세요
{
"mcpServers": {
"releaseguard": {
"command": "uvx",
"args": [
"releaseguard-cli",
"releaseguard",
"mcp"
]
}
}
}브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.
프로젝트의 .vscode/mcp.json에 붙여 넣으세요
{
"servers": {
"releaseguard": {
"type": "stdio",
"command": "uvx",
"args": [
"releaseguard-cli",
"releaseguard",
"mcp"
]
}
}
}브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.
이 서버는 내 컴퓨터에서 실행되는데, ChatGPT는 인터넷에 있는 서버에만 연결할 수 있어요. 다른 앱을 고르세요.
선택한 앱은 이 기기의 쿠키에만 저장돼요. 계정은 필요 없어요.
이 설정은 제작자의 레지스트리 정보로 만들었어요. README와 다르면 README를 따르세요.
내 컴퓨터에서 실행돼요. 설치하면 AI 앱에 도구 목록이 나타나요.
누구나 볼 수 있는 정보를 점검했고, 코드는 읽지 않았어요. 해당하지 않는 항목은 개수에서 뺐어요. · 2026년 11월 7일 점검
레지스트리 이름이 GitHub 계정 RudrenduPaul 기준으로 확인되었고, 이 계정이 저장소를 소유하고 있어요
아직 GitHub에서 이 저장소를 읽어 오지 않았어요
아직 GitHub에서 이 저장소를 읽어 오지 않았어요
내 컴퓨터에서 실행되는 서버라 인터넷 엔드포인트가 없어요
내 컴퓨터에서 실행되며, 대신 로컬 설정을 사용해요
내 컴퓨터에서 실행돼요. 도구 목록은 인터넷 엔드포인트가 있는 서버에서만 읽을 수 있어요
레지스트리에서 이 카테고리의 활성 서버 전체와 비교했어요. 스타는 게시자가 저장소를 소유한 경우에만 세요.
해당하는 기본 점검 1개 중 1개를 통과했어요. 코드는 읽지 않았어요. 키나 토큰을 넣기 전에 서버가 어떤 권한을 요구하는지 확인하고, 꼭 필요한 권한만 있는 키를 쓰세요.
레지스트리에 필수 키가 나와 있지 않아요. 다만 처음 쓸 때 로그인을 요구하는 서버도 있어요.
내 컴퓨터에서 실행돼요. AI 앱이 필요할 때 실행하고, 데이터는 제작자의 서버를 거치지 않아요(서버 자체가 온라인 서비스를 호출하는 경우는 제외).
GitHub 저장소는 마지막으로 – 변경됐고, 최신 버전(0.1.2)은 3개월 전 레지스트리에 올라왔어요.
Claude Desktop, Claude Code, Cursor, VS Code처럼 MCP를 지원하는 앱에서 쓸 수 있어요. 내 컴퓨터에 Python (uv)도 있어야 해요.
같은 카테고리에는: Spec Driven Development, Remote Desktop Commander, Zendesk
같은 카테고리에서 다른 제작자가 만든 서버예요. 스타 순으로 정렬했어요.
카드 보는 법:★ GitHub 스타i세 기둥: 리뷰 · 점검 · 사용 정도i업데이트 = 최신 버전을 레지스트리에 공개한 날i
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.
Zendesk MCP Pack — tickets, users, organizations via OAuth.
제작자의 저장소에서 쓰인 그대로 가져왔어요. PopNx MCP가 검토하거나 보증한 내용이 아니에요. HTML, 이미지, 스크립트는 제거했어요. 링크를 누르면 이 사이트를 벗어나요.
Install • Quickstart • CLI reference • Library API • Comparison • FAQ
Scan a dataset or model directory for PII and secrets with [Presidio](https://github.com/data-privacy-stack/presidio), redact what you find, and generate a public-release bundle, a Hugging Face dataset/model card plus an EU AI Act Art. 53(1)(d) training-data summary, in one command.
ReleaseGuard is not a PII detector. It's the glue between "I have a dataset I want to publish" and "I have a sanitized bundle with the paperwork already drafted." Detection is entirely Presidio's, an actively maintained open-source project with 10,000+ GitHub stars. ReleaseGuard chains Presidio's scan straight into redaction and into the two documents almost every public dataset/model release actually needs, instead of you writing a script to do it yourself.
pip install releaseguard-cli
python -m spacy download en_core_web_sm # Presidio's default NLP model (~13 MB, one-time)
npx releaseguard-cli --help
en_core_web_sm is spaCy's small English model and Presidio's own quickstart default. For higher-accuracy detection, install the larger model instead and pass --spacy-model:
python -m spacy download en_core_web_lg # ~400 MB, Presidio's recommendation for production
releaseguard scan ./data --spacy-model en_core_web_lg
This is a real, unedited run against a two-row sample CSV, not a mockup:
$ releaseguard scan dataset --score-threshold 0.4
Scanned 1 file(s) under dataset Total findings: 7
entity type count URL 3 PERSON 2 EMAIL_ADDRESS 2
That URL: 3 line is a real Presidio quirk worth calling out rather than hiding: its regex-based URL recognizer also fires on the domain portion of an email address (example.com inside alice.rivera@example.com), so email-heavy text double-counts as both EMAIL_ADDRESS and URL. This is Presidio's own recognizer behavior, unmodified, and it's worth noting entity counts can shift slightly between Presidio/spaCy versions since detection is NLP-based, not a fixed lookup table (this run used presidio-analyzer 2.2.364 with en_core_web_sm 3.8.0). Filter it out with --entities if you only care about email addresses:
releaseguard scan dataset --entities EMAIL_ADDRESS,PERSON,PHONE_NUMBER
Redact, then package a release bundle in one command:
$ releaseguard package dataset --output bundle --score-threshold 0.4
Release bundle written to bundle dataset card: bundle/README-dataset-card.md EU AI Act Art. 53(1)(d) summary: bundle/eu-ai-act-training-summary.md redacted source: bundle-redacted-source
bundle/eu-ai-act-training-summary.md opens like this, real scan counts filled in, everything else left as an explicit placeholder for a human to complete:
# Training Data Summary (EU AI Act Art. 53(1)(d))
_Draft generated by ReleaseGuard... Based on the European Commission's training-data-summary template, published 2025-07-24. This covers the narrow, currently-binding categorical-summary requirement only -- it is not a claim of full training-data disclosure._
A Presidio-backed scan (detector: presidio) covered 1 file(s) under dataset.
| PII/secret category detected | Occurrences |
|---|---|
EMAIL_ADDRESS | 2 |
PERSON | 2 |
URL | 3 |
--json on every command switches to machine-readable output for scripts and agents.
$ releaseguard --help
Usage: releaseguard [OPTIONS] COMMAND [ARGS]...
Scan, redact, and package a dataset/model directory for public release.
ReleaseGuard is a packaging layer on top of Presidio (originally a Microsoft project, now maintained by data-privacy-stack) -- it does not detect PII independently of Presidio. See the README's "What ReleaseGuard is not" section.
Options: --version Show the version and exit. --help Show this message and exit.
Commands: mcp Start an MCP server exposing scan/redact/package as agent tools. package Scan PATH, optionally redact it, and generate a release bundle... redact Scan PATH and write a redacted copy to --output. scan Scan PATH (a file or directory) for PII and secrets using...
| Command | Purpose | ||
|---|---|---|---|
scan PATH | Scans a file or directory (CSV, JSON/JSONL, plain text) with Presidio. --entities, --score-threshold, --spacy-model, --json. | ||
redact PATH --output DIR | Scans, then writes a redacted copy to DIR. Never touches PATH. `--strategy mask\ | hash\ | remove, --overwrite`. |
package PATH --output DIR | Scans (and by default redacts first), then writes a Hugging Face dataset/model card plus the EU AI Act Art. 53(1)(d) summary to DIR. `--kind dataset\ | model\ | both, --redact-first/--no-redact-first`. |
mcp | Starts an MCP server (stdio) exposing scan_directory_tool, redact_directory_tool, package_release_tool. Requires pip install "releaseguard-cli[mcp]" on Python 3.10+. |
Every command supports --json. Full flag reference: releaseguard --help.
from releaseguard.detectors import get_detector
from releaseguard.scanner import scan_directory from releaseguard.redactor import redact_directory from releaseguard.packager import build_release_bundle from releaseguard.types import RedactionStrategy
detector = get_detector("presidio", score_threshold=0.4) scan_result = scan_directory("dataset/", detector)
redaction_result = redact_directory( scan_result, "dataset-redacted/", strategy=RedactionStrategy.MASK )
bundle = build_release_bundle( scan_result, "bundle/", redaction_result=redaction_result, source_kind="dataset" ) print(bundle.eu_ai_act_summary_path)
PIIDetector (releaseguard.detectors.base) and FileReader (releaseguard.readers.base) are the two extension points. Presidio is currently the only detector shipped; CSV, JSON/JSONL, and plain text are currently the three readers shipped. Both are registries, not hardcoded calls, specifically so a new format or a second detection backend is a scoped addition later. See CONTRIBUTING.md.
ReleaseGuard ships a Model Context Protocol server so an AI agent (Claude, Cursor, or any MCP-compatible client) can scan, redact, and package a dataset or model directory directly, without a human invoking the CLI by hand.
Install the extra:
pip install "releaseguard-cli[mcp]"
Start it directly with the mcp subcommand:
releaseguard mcp
Add it to your MCP client's config (for Claude Desktop, claude_desktop_config.json). The server is started via a subcommand of the published releaseguard console script, not a separate console script of its own:
{
"mcpServers": { "releaseguard": { "command": "uvx", "args": ["--from", "releaseguard-cli", "releaseguard", "mcp"] } } }
Transport is stdio, so there is nothing to host: the MCP client spawns the server as a local subprocess. Source: src/releaseguard/mcp_server.py.
The server exposes three tools, each returning the same JSON shape as the matching CLI --json output:
| Tool | Purpose |
|---|---|
scan_directory_tool(path, spacy_model=None, score_threshold=0.35) | Scan a directory for PII and secrets with Presidio. |
redact_directory_tool(path, output, strategy="mask", overwrite=False) | Scan, then write a redacted copy to output. Never mutates path. strategy is "mask", "hash", or "remove". |
package_release_tool(path, output, kind="dataset", redact_first=True, strategy="mask") | Scan, optionally redact, and write a release bundle (dataset/model card plus the EU AI Act Art. 53(1)(d) summary) to output. |
A real example call:
scan_directory_tool(path="./dataset", score_threshold=0.4)
-> {"files_scanned": 1, "root_path": "./dataset", "findings": [...], "entity_counts": {"URL": 3, "PERSON": 2, "EMAIL_ADDRESS": 2}}
Every tool checks that path exists before doing anything else and returns a structured error, {"error": "Path ' ' does not exist.", "error_type": "PathNotFound"}, instead of a silent, misleading "0 files scanned" success result. Earlier versions of this server skipped that check and let a bad path slip straight into scan_directory, which returned a false success instead of an error; every tool handler is also now wrapped so an unexpected exception comes back as {"error": ..., "error_type": ...} rather than crashing the server. A .well-known/agent.json manifest is shipped at the repo root for A2A-style discovery, listing both the CLI and MCP interfaces and the packages that provide them.
| ReleaseGuard | Presidio | huggingface_hub card tooling | pii-lib | |
|---|---|---|---|---|
| Detects PII/secrets | No, wraps Presidio | Yes (its own job) | No | Yes (regex + NER, code-training-data scoped) |
| Redacts detected entities | Yes (via presidio-anonymizer) | Yes (library-level) | No | Yes |
| Generates a Hugging Face dataset/model card | Yes, from real scan results | No | Yes (manual fields, no scan integration) | No |
| Generates an EU AI Act Art. 53(1)(d) summary | Yes, from real scan results | No | No | No |
| One command, scan through release bundle | Yes | No (library only, you write the glue) | No (library only) | No |
| GitHub stars (checked 2026-08) | New in 2026 | ~10,300 | ~3,800 | 16 |
Star counts checked live against the GitHub API on 2026-08-03: data-privacy-stack/presidio (originally microsoft/presidio; the project moved organizations, same codebase), huggingface/huggingface_hub, [bigcode-pro
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