Which integrations are available for this server?

Enables data compliance tools to be used as LangChain tools via langchain-mcp-adapters. Supports data compliance tools within LangGraph agents through langchain-mcp-adapters. Integrates with OpenAI Agents SDK via HostedMCPTool to add data compliance checks to agents.

How do I use Data Compliance Classifier MCP?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Data Compliance Classifier MCP Check if this customer data is safe to store" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

Data Compliance Classifier MCP

by OjasKord

Overview Schema Source Related Servers Score Discussions Admin

JavaScript

Remote

Data Compliance Classifier MCP

ToolRank

Your agent is about to store customer data. Is it safe to? This tool tells you in one call.

What it does

Before your agent stores, transmits, logs, or passes any data to another system — call validate_data_safety. Get back a clear verdict: SAFE_TO_PROCESS, REDACT_BEFORE_PASSING, DO_NOT_STORE, or ESCALATE. Your agent acts on the verdict immediately. No human interpretation needed.

Prevents GDPR, HIPAA, and PCI-DSS violations before they happen — not after.

Related MCP server: PII Redaction MCP Server

Why this exists

Autonomous agents handle data from users, APIs, forms, and external sources constantly. Most agents process that data without checking whether they should. When something goes wrong — a GDPR breach, a leaked credential, a PII write to an unencrypted store — it's already too late.

This tool gives agents a pre-action safety check. One call, clear verdict, agent proceeds or halts.

Tools

`validate_data_safety` (free tier)

Call this BEFORE your agent stores, transmits, or passes any data payload.

Input:

payload — the data to classify (any string, JSON, form data, API response)
context — what your agent is about to do with it (improves accuracy)
data_origin_ip — optional IP for jurisdiction detection (GDPR if EU, CCPA if US, etc.)
jurisdiction — optional override if IP unavailable

Output:

verdict — SAFE_TO_PROCESS / REDACT_BEFORE_PASSING / DO_NOT_STORE / ESCALATE
sensitivity_level — PUBLIC / INTERNAL / CONFIDENTIAL / RESTRICTED
detected_categories — PII, PHI, PCI, CREDENTIALS, FINANCIAL, LOCATION, etc.
applicable_regulations — GDPR, HIPAA, PCI-DSS, CCPA, PIPEDA, LGPD, etc.
recommended_action — one sentence telling your agent exactly what to do next
jurisdiction_detected — country detected from IP
credential_check — breach status from HaveIBeenPwned k-anonymity API
patterns_detected — pre-screened PII patterns found

`get_safety_report` (paid tier)

Batch classification for up to 50 payloads plus audit-ready compliance reports.

Modes:

BATCH — classify multiple payloads with full AI reasoning + AbuseIPDB threat intelligence
AUDIT — generate a structured compliance report for a dataset description

`validate_data_safety_lite` (free tier)

Pattern-only screening for high-volume payload batches -- no AI classification, no IP check, no jurisdiction lookup. Returns SAFE_TO_PROCESS / REVIEW_REQUIRED in under 100ms. Use to filter large batches before selectively running validate_data_safety on flagged items.

Data privacy

We do not store or log your data payloads. All payloads are analysed in memory and immediately discarded. Credential checks use the HaveIBeenPwned k-anonymity API — your credentials are never transmitted in full. Only the first 5 characters of a SHA-1 hash are sent.

Data sources

Claude AI — sensitivity classification and regulatory mapping
IPinfo (ipinfo.io) — jurisdiction detection from IP address
HaveIBeenPwned (haveibeenpwned.com) — credential breach checking via k-anonymity
AbuseIPDB (abuseipdb.com) — IP threat intelligence (paid tier)

Pricing

Plan	Classifications	Price
Free	20/month	No API key needed
Starter	500-call bundle	$24
Pro	2,000-call bundle	$84

Upgrade at kordagencies.com

Quick start

No API key needed for free tier:

{
  "data-compliance": {
    "url": "https://data-compliance-mcp-production.up.railway.app"
  }
}

With paid API key:

{
  "data-compliance": {
    "url": "https://data-compliance-mcp-production.up.railway.app",
    "headers": {
      "x-api-key": "your_api_key_here"
    }
  }
}

Harness Integration

Claude Code / Claude Desktop (.mcp.json)

{
  "mcpServers": {
    "data-compliance": {
      "type": "http",
      "url": "https://data-compliance-mcp-production.up.railway.app"
    }
  }
}

LangChain (Python)

from langchain_mcp_adapters.client import MultiServerMCPClient
client = MultiServerMCPClient({
    "data-compliance": {
        "url": "https://data-compliance-mcp-production.up.railway.app",
        "transport": "http"
    }
})
tools = await client.get_tools()

OpenAI Agents SDK (Python)

from agents import Agent, HostedMCPTool
agent = Agent(
    name="Assistant",
    tools=[HostedMCPTool(tool_config={
        "type": "mcp",
        "server_label": "data-compliance",
        "server_url": "https://data-compliance-mcp-production.up.railway.app",
        "require_approval": "never"
    })]
)

LangGraph

Same as LangChain above — langchain-mcp-adapters works with LangGraph natively.

Example call

curl -X POST https://data-compliance-mcp-production.up.railway.app \
  -H "Content-Type: application/json" \
  -d '{"jsonrpc":"2.0","id":1,"method":"tools/call","params":{"name":"validate_data_safety","arguments":{"payload":"{\"name\":\"John Smith\",\"email\":\"john@example.com\",\"dob\":\"1985-03-12\",\"address\":\"14 Baker Street, London\"}","context":"write to customer database","jurisdiction":"EU"}}}'

Expected response:

{
  "verdict": "DO_NOT_STORE",
  "sensitivity_level": "RESTRICTED",
  "detected_categories": ["PII"],
  "applicable_regulations": ["GDPR"],
  "recommended_action": "Do not store without explicit consent and a documented lawful basis under GDPR Article 6.",
  "jurisdiction_detected": "EU"
}

Legal

Classification is AI-powered and for informational purposes only. Does not constitute legal advice and does not guarantee regulatory compliance. Full terms: kordagencies.com/terms.html

Available Tools

3 tools

get_safety_reportAInspect

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. Transmitting a flagged payload without the redaction strategy and compliant reformulation this tool returns creates an unremediated violation with no audit trail showing what was flagged or fixed. Do not proceed with the original payload after a non-SAFE verdict without running this tool first.

ParametersJSON Schema

Name	Required	Description
`mode`	Yes	REPORT: get redaction strategy and compliant reformulation for a flagged payload. BATCH: classify up to 50 payloads. AUDIT: generate compliance summary report.
`context`	No	What will be done with this data. Used to improve verdict accuracy.
`payload`	No	The flagged payload to analyse. Required for REPORT mode.
`payloads`	No	Array of data payloads to classify. Required for BATCH mode. Maximum 50.
`jurisdiction`	No	Jurisdiction override for REPORT mode (e.g. "EU", "US", "UK"). Optional.
`dataset_description`	No	Description of the dataset for AUDIT mode (e.g. "customer CRM records including name, email, purchase history, and UK addresses").

Output Schema

ParametersJSON Schema

Name	Required	Description
`mode`	No
`status`	No	Present on the free-tier REPORT preview path
`message`	No
`checked_at`	Yes
`_disclaimer`	Yes
`upgrade_url`	No
`patterns_detected`	No

TDQS

A4.8/5.0

Behavior5/5

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

No annotations provided, so description carries full burden. It explains what the tool returns (regulation triggered, problematic fields, redaction strategy, compliant reformulation) and the consequences of not using it (unremediated violation, no audit trail).

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Several sentences but each adds value. Front-loaded with purpose, then usage guidelines, then outcomes. Could be slightly more concise, but no superfluous content.

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 6 params (1 required), 100% schema coverage, and output schema exists, description fully covers when to use, what it returns, and consequences. No missing guidance.

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 100%, baseline 3. Description adds value by explaining workflow linking modes (REPORT, BATCH, AUDIT) to prior tool usage, though schema already documents enums. Extra context like 'immediately after validate_data_safety' goes beyond schema.

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 retrieves a 'detailed compliance report and remediation strategy' for a 'flagged payload'. It uses a specific verb ('Retrieves') and resource, and distinguishes from siblings by positioning it after 'validate_data_safety' returns non-SAFE verdicts.

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?

Explicitly states when to call: after 'validate_data_safety' returns 'REDACT_BEFORE_PASSING, DO_NOT_STORE, or ESCALATE'. Warns against proceeding without it, providing clear context for use.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

validate_data_safetyAInspect

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.

ParametersJSON Schema

Name	Required	Description
`context`	No	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.
`payload`	Yes	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.
`jurisdiction`	No	Override jurisdiction if known (e.g. "EU", "US", "UK", "CA", "AU"). Use if data_origin_ip is unavailable but jurisdiction is known.
`data_origin_ip`	No	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.

Output Schema

ParametersJSON Schema

Name	Required	Description
`verdict`	Yes
`reasoning`	No	Paid tier only -- gated to _reasoning_gated on free tier
`checked_at`	Yes
`confidence`	No
`source_url`	No
`_disclaimer`	Yes
`analysis_type`	No
`credential_check`	No
`patterns_detected`	No
`redaction_targets`	No
`sensitivity_level`	Yes
`recommended_action`	No
`detected_categories`	No
`jurisdiction_detected`	No
`applicable_regulations`	No

TDQS

A4.4/5.0

Behavior4/5

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

No annotations are provided, so the description bears full responsibility. It discloses that payloads are analyzed in memory and immediately discarded, and it explains the consequences of not validating ('unrecoverable regulatory exposure'). It also states the verdict is machine-ready, though it does not detail required permissions or potential side effects.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the core purpose and then structured into usage timing, return values, and next steps. Each sentence adds value, though it is somewhat lengthy. It avoids redundancy with the schema and annotations.

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's complexity (multiple frameworks, multiple verdicts) and the presence of an output schema, the description is remarkably complete. It covers regulatory scope, return values, usage context, data handling policy, and escalation path. No important aspects are omitted.

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 description coverage is 100%, providing baseline clarity. The description adds extra value by explaining how each parameter improves verdict accuracy (e.g., 'context improves verdict accuracy', 'payload never stored', 'data_origin_ip improves regulatory accuracy'). This goes beyond the 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 validates a data payload against multiple regulatory frameworks (GDPR, HIPAA, PCI-DSS, etc.), specifying the verb 'validates' and the resource 'data payload'. It distinguishes itself from siblings by mentioning the follow-up tool get_safety_report, indicating a distinct workflow.

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?

The description provides explicit when-to-use instructions: 'Call this BEFORE your agent passes any assembled payload to an external API... at the moment the payload is assembled'. It also guides the next step if the verdict is not SAFE_TO_PROCESS. However, it lacks explicit when-not-to-use scenarios or direct comparison with the 'lite' sibling.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

validate_data_safety_liteAInspect

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. Treating a SAFE_TO_PROCESS result here as a full verdict lets sensitive data outside these regex patterns -- contextual PII, non-standard credential formats -- reach an external endpoint undetected, with no chance to intercept it afterward. 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.

ParametersJSON Schema

Name	Required	Description	Default
`context`	No	Optional: what your agent plans to do with this data.
`payload`	Yes	The data payload to screen for sensitive patterns.

Output Schema

ParametersJSON Schema

Name	Required	Description
`verdict`	Yes
`checked_at`	Yes
`_disclaimer`	Yes
`agent_action`	Yes
`analysis_type`	No
`patterns_detected`	No
`sensitivity_level`	No

TDQS

A4.8/5.0

Behavior5/5

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

No annotations provided, so description fully discloses behavioral traits: <100ms, no AI, no IP check, no jurisdiction lookup. Warns that SAFE_TO_PROCESS result can miss contextual PII and is not a full verdict.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Slightly long but every sentence earns its place. Front-loaded with core purpose and usage. No fluff. Could be slightly tighter but still well-structured.

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?

Output schema exists, so return values not required but description mentions outputs SAFE_TO_PROCESS/REVIEW_REQUIRED. Also explains risk of false positives. Complete given complexity.

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 100%, baseline 3. Description adds context for 'context' parameter and explains purpose of 'payload'. Adds value beyond 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?

Clearly states tool validates payloads for sensitive patterns without AI classification. Distinguishes from sibling 'validate_data_safety' which presumably uses AI. Specific verb 'validate' and resource 'payload'.

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?

Explicitly says when to use (pre-screening high-volume payloads when pattern detection sufficient) and when not (regulated environments). Directs to alternative validate_data_safety for flagged items.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

3 tool updatesv1.0.32Jul 29, 2026
- First observedget_safety_report
- First observedvalidate_data_safety
- First observedvalidate_data_safety_lite

Install Server

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

TDQS

A4.6/5.0

Disambiguation5/5

Each tool has a clearly distinct purpose: validate_data_safety performs full AI classification, get_safety_report provides detailed compliance reports for flagged payloads, and validate_data_safety_lite offers fast pattern-only pre-screening. There is no overlap or ambiguity.

Naming Consistency5/5

All tool names use a consistent verb_noun pattern in snake_case (validate_data_safety, get_safety_report, validate_data_safety_lite). The 'lite' suffix is a clear modifier that maintains consistency.

Tool Count4/5

Three tools is slightly on the lower end but appropriate for a focused compliance classifier. The set covers the core workflow without being too sparse or overloaded.

Completeness4/5

The tool set covers the primary validation and reporting workflow comprehensively. Minor gaps exist, such as the lack of configuration or audit tools, but the core compliance functionality is complete for typical use cases.

Maintenance

ActivityActive

ResponsivenessNo issues

Resources

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If you are the server author, to access and configure the admin panel.

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Data Compliance Classifier MCP

What it does

Why this exists

Tools

validate_data_safety (free tier)

get_safety_report (paid tier)

validate_data_safety_lite (free tier)

Data privacy

Data sources

Pricing

Quick start

Harness Integration

Claude Code / Claude Desktop (.mcp.json)

LangChain (Python)

OpenAI Agents SDK (Python)

LangGraph

Example call

Legal

Available Tools

Output Schema

TDQS

Output Schema

TDQS

Output Schema

TDQS

Tool Schema Changelog

TDQS

Maintenance

Resources

Looking for Admin?

Related MCP Connectors

Related MCP Servers

@actalumen/mcp-serverofficial

PII Redaction MCP Server

safeguard-mcp

suprawall

Latest Blog Posts

MCP directory API

`validate_data_safety` (free tier)

`get_safety_report` (paid tier)

`validate_data_safety_lite` (free tier)