Data Compliance Classifier MCP
Server Details
Classify data safety before storing or sharing. GDPR, HIPAA, PCI-DSS, CCPA. AI-powered.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Tool Definition Quality
Average 4.5/5 across 2 of 2 tools scored.
The two tools have clearly distinct purposes with no overlap. get_safety_report is for bulk classification and audit report generation, while validate_data_safety is for pre-processing safety validation of individual data payloads. An agent can easily differentiate between batch reporting workflows and immediate safety screening needs.
Both tools follow a consistent verb_noun pattern (get_safety_report, validate_data_safety) with clear action-oriented verbs. The naming is predictable and readable throughout the tool set, making it easy for agents to understand what each tool does based on its name alone.
With only 2 tools, the server feels under-scoped for its stated domain of 'Data Compliance Classifier'. While the tools cover distinct functions, a comprehensive compliance system would typically require more operations such as configuration management, historical report retrieval, or policy validation tools. The count is too low for the apparent scope.
The tools cover two important workflows (bulk reporting and pre-processing validation), but there are notable gaps in the compliance surface. Missing operations include updating classification policies, retrieving past reports, managing compliance rules, or handling edge cases like appeals or overrides. Agents can work with the provided tools but will encounter limitations in full compliance lifecycle management.
Available Tools
2 toolsget_safety_reportAInspect
Call this tool when your agent needs to classify a batch of data payloads and generate an audit-ready compliance report. Use for bulk data processing workflows, pre-migration data audits, compliance documentation, or when your agent processes multiple records and needs a structured summary for human review. Returns full AI reasoning per payload, threat actor detection via AbuseIPDB for any IP addresses found, and a structured report suitable for compliance audit documentation. Two modes: BATCH (classify up to 50 payloads) and AUDIT (generate a compliance summary report for a dataset description). AI-powered analysis — NOT a simple database lookup. LEGAL NOTICE: Classification is informational only. We do not store your data payloads. Full terms: kordagencies.com/terms.html. Paid API key required — upgrade at kordagencies.com.
| Name | Required | Description | Default |
|---|---|---|---|
| mode | Yes | BATCH: classify up to 50 payloads with full reasoning. AUDIT: generate compliance summary report. | |
| context | No | What will be done with this data. Used to improve verdict accuracy. | |
| payloads | No | Array of data payloads to classify. Required for BATCH mode. Maximum 50. | |
| dataset_description | No | Description of the dataset for AUDIT mode (e.g. "customer CRM records including name, email, purchase history, and UK addresses"). |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behavioral traits: AI-powered analysis (not a simple lookup), returns full AI reasoning and threat actor detection, includes legal notices about data storage and terms, and mentions a paid API key requirement. It doesn't cover rate limits or error handling, but provides substantial context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized but not optimally structured. It front-loads the purpose and usage guidelines effectively, but includes legal and commercial details that could be separated. Every sentence adds value, but the organization could be improved for better readability.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (4 parameters, no annotations, no output schema), the description provides substantial context about purpose, usage, behavioral traits, and limitations. It adequately compensates for the lack of output schema by describing what the tool returns. The main gap is the absence of explicit error handling or rate limit information.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds some context by mentioning the 50-payload limit for BATCH mode and the compliance summary purpose for AUDIT mode, but doesn't provide significant additional parameter semantics beyond what's in the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('classify a batch of data payloads and generate an audit-ready compliance report') and distinguishes it from the sibling tool 'validate_data_safety' by emphasizing bulk processing, AI-powered analysis, and structured reporting rather than simple validation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly lists when to use this tool ('bulk data processing workflows, pre-migration data audits, compliance documentation, or when your agent processes multiple records and needs a structured summary for human review') and distinguishes between two modes (BATCH and AUDIT) with clear use cases, providing comprehensive guidance on alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
validate_data_safetyAInspect
Call this tool BEFORE your agent stores, transmits, logs, or passes any data payload to another system. Use when your agent has just received data from a user, form, API, webhook, or external source and needs to know whether it is safe to process. Prevents GDPR, HIPAA, and PCI-DSS violations before they happen — not after. Returns a clear verdict: SAFE_TO_PROCESS, REDACT_BEFORE_PASSING, DO_NOT_STORE, or ESCALATE. Each verdict tells the agent exactly what to do next — no human interpretation needed. Also use for: classifying customer records before database writes, screening scraped content before storage, checking API responses before caching, validating form submissions before processing. AI-powered analysis — NOT a simple pattern match. Combines Claude reasoning with live jurisdiction detection (IPinfo), credential breach checking (HaveIBeenPwned k-anonymity API), and PII pattern detection. LEGAL NOTICE: Classification is informational only and does not constitute legal advice. We do not store your data payload. Full terms: kordagencies.com/terms.html. Free tier: first 20 classifications/month, no API key needed.
| Name | Required | Description | Default |
|---|---|---|---|
| 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. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure and excels at this. It explains the tool's analysis methodology (AI-powered, not simple pattern matching), the technologies used (Claude reasoning, IPinfo, HaveIBeenPwned API, PII detection), legal disclaimers, data handling (no storage), and usage limits (free tier: 20 classifications/month). It also describes the return verdicts and their implications for agent actions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately front-loaded with core functionality and usage guidelines, followed by implementation details and legal notices. While comprehensive, some sentences about legal terms and free tier could be considered secondary information. However, all content is relevant to tool understanding, and the structure effectively guides the reader from purpose to practical details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (safety validation with regulatory implications) and lack of annotations/output schema, the description provides complete context. It covers purpose, usage, behavioral traits, analysis methodology, return values (SAFE_TO_PROCESS, REDACT_BEFORE_PASSING, DO_NOT_STORE, ESCALATE), data handling policies, legal disclaimers, and usage limits. No additional information is needed for an agent to understand and invoke this tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the baseline is 3. The description adds significant value by explaining the overall purpose of parameter usage: to improve verdict accuracy through context and jurisdiction detection. While it doesn't detail individual parameters beyond what the schema provides, it frames their collective role in the safety analysis process, justifying a score above baseline.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: to validate data safety before processing. It specifies the verb 'validate' and resource 'data safety', distinguishing it from the sibling 'get_safety_report' which likely retrieves historical reports rather than performing real-time validation. The description explicitly mentions preventing GDPR, HIPAA, and PCI-DSS violations, which clarifies its regulatory compliance focus.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance on when to use this tool: BEFORE storing, transmitting, logging, or passing data to another system, specifically when data is received from various sources (user, form, API, webhook, external source). It also lists specific use cases like classifying customer records, screening scraped content, checking API responses, and validating form submissions. No when-not guidance is needed as this is a pre-processing safety check.
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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