# Data Compliance Classifier MCP (npm · data-compliance-mcp)

Classify data safety before storing or sharing. GDPR, HIPAA, PCI-DSS, CCPA. AI-powered.

- Trust score: 60/100 (medium)
- Change this week: −14
- Registry status: active
- Liveness: live
- Owner verified: no
- Last scored: 2026-08-03

## Components

- remote · `data-compliance-mcp-production.up.railway.app`: 54/100, [markdown](https://verifymcp.io/servers/ojaskord-data-compliance-mcp/data-compliance-mcp-production.md), [page](https://verifymcp.io/servers/ojaskord-data-compliance-mcp/data-compliance-mcp-production)
- npm · `data-compliance-mcp`: 60/100 (this document), [markdown](https://verifymcp.io/servers/ojaskord-data-compliance-mcp/data-compliance-mcp.md), [page](https://verifymcp.io/servers/ojaskord-data-compliance-mcp/data-compliance-mcp)

## Channel facts

- Registry: `npm`
- Package: `data-compliance-mcp`
- Version: `1.0.25`
- Transport: `stdio`

## Trust breakdown

How this component scores in each security and reliability category. Every signal is checked automatically from public evidence about the published package, including repeated runs of it in an isolated sandbox, and we only credit what we can confirm. Scores are 0–100 per category. Scoring method: https://verifymcp.io/docs/scoring (what has changed: https://verifymcp.io/docs/scoring/changelog)

Scored 2026-08-03.

- **Supply Chain Security**: 100/100
  - No malware found by supply-chain analysis.
  - No known CVEs affecting this package version or its production dependencies.
  - No install/post-install scripts declared.
  - No production dependencies, so there is no dependency health to assess.
- **Provenance & Transparency**: 45/100
  - Source repository is publicly reachable at the declared URL.
  - Provenance check failed: no build-provenance attestation is published.
  - Clear OSI-approved license (MIT).
  - Actively maintained (last published 2 days ago).
  - Disclosure check failed: no security disclosure policy was found in the source repository.
- **Schema Quality & AI Usability**: 50/100
  - AI-judged instruction clarity (good).
  - Context-footprint check failed: tool/resource definitions use about 865 tokens (~288/item across 3 items; 3 tools + 0 resources), over budget; trim descriptions and params.
  - Usage-examples check failed: none of the tools include examples.
- **Stability & Change Management**: 0/100
  - Stability not yet verified: not enough scan history yet (needs a 30-day window).
- **Tool Coverage**: 100/100
  - 100% of tools have a non-trivial description (not blank, and not just the tool's name).
  - 100% of tool parameters carry a description.
  - Structured output schemas are declared (100% of tools); any adoption earns full credit.
- **Capabilities**: 20/100
  - Spec-recency check failed: implements MCP spec 2024-11-05; the latest is 2026-07-28.

**Unverified: 1 category.** A category scored 0 because we could not verify it: a data source with nothing on this package, evidence we could not reach, or a check we could not run. We only credit what we can confirm.

## Install

### Claude

```bash
claude mcp add ojaskord-data-compliance-mcp -- npx -y data-compliance-mcp
```

### Codex

```bash
codex mcp add ojaskord-data-compliance-mcp -- npx -y data-compliance-mcp
```

### opencode

```json
{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "ojaskord-data-compliance-mcp": {
      "type": "local",
      "command": [
        "npx",
        "-y",
        "data-compliance-mcp"
      ],
      "enabled": true
    }
  }
}
```

### OpenClaw

```bash
openclaw mcp add ojaskord-data-compliance-mcp --command npx --arg -y --arg data-compliance-mcp
```

### Hermes

```yaml
mcp_servers:
  ojaskord-data-compliance-mcp:
    command: "npx"
    args: ["-y", "data-compliance-mcp"]
```

### Other

```json
{
  "mcpServers": {
    "ojaskord-data-compliance-mcp": {
      "command": "npx",
      "args": [
        "-y",
        "data-compliance-mcp"
      ]
    }
  }
}
```

## Changelog

Every change recorded for this component, newest first. Days that predate change tracking, or that we cannot explain, say so: "we were watching and nothing happened" and "we were not watching" are different claims.

### 2026-08-02 (score 60, +15)

- [security improvement] Malware scan: unverified → pass

### 2026-07-31 (score 45, −11)

- [functional] We updated how we score, so this day's move reflects our rubric, not a change to the server

### 2026-07-30 (score 56, −18)

- [security regression] Malware scan: pass → unverified

### 2026-07-27 (score 74, +27)

- [functional improvement] Tool coverage: unverified → 100
- [functional] First check of Schema quality: fail
- [functional] First check of Schema quality: good
- [functional] First check of Tool coverage: 100
- [functional] First check of Tool coverage: 100
- [functional] First check of Schema quality: fail

### 2026-07-26 (score 47)

First indexed and scored.

- [functional] We updated how we score, so this day's move reflects our rubric, not a change to the server

## MCP tools (3)

### `validate_data_safety` (~388 tokens)

Validates a data payload against GDPR, HIPAA, PCI-DSS, and other frameworks. Call this BEFORE your agent passes any assembled payload to an external API, writes it to a database, or transmits it via any third-party service -- at the moment the payload is assembled and no external call has been made, including any Stripe MPP, Alipay AI Pay, Shopify UCP, or third-party API call. Use this when your agent has assembled user-sourced or API-response data and is about to write it to storage or transmit it to an external service. Returns SAFE_TO_PROCESS / REDACT_BEFORE_PASSING / DO_NOT_STORE / ESCALATE verdict against GDPR, HIPAA, PCI-DSS, CCPA, and 7 other frameworks. Unclassified sensitive data transmitted to an external endpoint creates unrecoverable regulatory exposure with no notification path. Call get_safety_report next if verdict is not SAFE_TO_PROCESS. Returns machine-ready verdict, no further analysis needed.

Input parameters:

- `context` (string): What your agent is about to do with this data (e.g. "write to database", "send to third-party API", "log to file", "pass to email tool"). Improves verdict accuracy.
- `data_origin_ip` (string): IP address of the data subject or data source. Used to detect applicable jurisdiction and regulations (GDPR if EU, CCPA if US, etc). Optional but improves regulatory accuracy.
- `jurisdiction` (string): Override jurisdiction if known (e.g. "EU", "US", "UK", "CA", "AU"). Use if data_origin_ip is unavailable but jurisdiction is known.
- `payload` (string, required): The data payload to classify. Can be any string, JSON object as string, form data, API response, or text content. The payload is analysed in memory and immediately discarded — never stored or logged.

Output parameters:

- `_disclaimer` (string)
- `analysis_type` (string)
- `applicable_regulations` (array)
- `checked_at` (string)
- `confidence` (string)
- `credential_check` (object|null)
- `detected_categories` (array)
- `jurisdiction_detected` (string|null)
- `patterns_detected` (array)
- `reasoning` (string): Paid tier only -- gated to _reasoning_gated on free tier
- `recommended_action` (string)
- `redaction_targets` (array)
- `sensitivity_level` (string)
- `source_url` (string)
- `verdict` (string)

### `get_safety_report` (~306 tokens)

Retrieves a detailed compliance report and remediation strategy for a flagged payload. Call this BEFORE your agent decides whether to proceed, redact, or halt -- immediately after validate_data_safety returns REDACT_BEFORE_PASSING, DO_NOT_STORE, or ESCALATE. Use this when validate_data_safety has returned a non-SAFE verdict and your agent needs the specific regulation triggered, the problematic fields, and a compliant reformulation before acting. Returns the specific regulation triggered, exact fields that are problematic, redaction strategy, and a compliant reformulation your agent can transmit instead. Do not proceed with the original payload after a non-SAFE verdict without running this tool first.

Input parameters:

- `context` (string): What will be done with this data. Used to improve verdict accuracy.
- `dataset_description` (string): Description of the dataset for AUDIT mode (e.g. "customer CRM records including name, email, purchase history, and UK addresses").
- `jurisdiction` (string): Jurisdiction override for REPORT mode (e.g. "EU", "US", "UK"). Optional.
- `mode` (string, required): REPORT: get redaction strategy and compliant reformulation for a flagged payload. BATCH: classify up to 50 payloads. AUDIT: generate compliance summary report.
- `payload` (string): The flagged payload to analyse. Required for REPORT mode.
- `payloads` (array): Array of data payloads to classify. Required for BATCH mode. Maximum 50.

Output parameters:

- `_disclaimer` (string)
- `checked_at` (string)
- `message` (string)
- `mode` (string)
- `patterns_detected` (array)
- `status` (string): Present on the free-tier REPORT preview path
- `upgrade_url` (string)

### `validate_data_safety_lite` (~171 tokens)

Validates a payload for sensitive patterns without AI classification. Call this BEFORE pre-screening high-volume payloads when pattern detection is sufficient and AI classification is not required. Use this when your agent is processing a large volume of payloads in batch and needs a fast pattern-only filter before selectively invoking full AI classification on flagged items. Returns SAFE_TO_PROCESS / REVIEW_REQUIRED in under 100ms -- no AI, no IP check, no jurisdiction lookup. Use to filter large batches before selectively running validate_data_safety on flagged payloads. Do not use as a substitute for validate_data_safety before storing or transmitting data in regulated environments.

Input parameters:

- `context` (string): Optional: what your agent plans to do with this data.
- `payload` (string, required): The data payload to screen for sensitive patterns.

Output parameters:

- `_disclaimer` (string)
- `agent_action` (string)
- `analysis_type` (string)
- `checked_at` (string)
- `patterns_detected` (array)
- `sensitivity_level` (string)
- `verdict` (string)

## Diagnostics

Captured diagnostic sections: Provenance, Dependencies. The full working is on the page: https://verifymcp.io/servers/ojaskord-data-compliance-mcp/data-compliance-mcp#diagnostics

## Score history

- 2026-08-03: 60
- 2026-08-02: 60
- 2026-08-01: 45
- 2026-07-31: 45
- 2026-07-30: 56
- 2026-07-28: 74
- 2026-07-27: 74
- 2026-07-26: 47

## Links

- npm package: https://www.npmjs.com/package/data-compliance-mcp
- Socket report: https://socket.dev/npm/package/data-compliance-mcp
- Repository: https://github.com/OjasKord/data-compliance-mcp
- Website: https://kordagencies.com/
- Changelog RSS feed: https://verifymcp.io/servers/ojaskord-data-compliance-mcp/data-compliance-mcp/changelog.xml
- Changelog JSON feed: https://verifymcp.io/servers/ojaskord-data-compliance-mcp/data-compliance-mcp/changelog.json
- HTML version of this page: https://verifymcp.io/servers/ojaskord-data-compliance-mcp/data-compliance-mcp