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
Glama MCP Gateway
Connect through Glama MCP Gateway for full control over tool access and complete visibility into every call.
Full call logging
Every tool call is logged with complete inputs and outputs, so you can debug issues and audit what your agents are doing.
Tool access control
Enable or disable individual tools per connector, so you decide what your agents can and cannot do.
Managed credentials
Glama handles OAuth flows, token storage, and automatic rotation, so credentials never expire on your clients.
Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Tool Definition Quality
Average 4.5/5 across 3 of 3 tools scored.
Each tool has a distinct purpose: validate_data_safety for full AI classification, validate_data_safety_lite for fast pattern-based pre-screening, and get_safety_report for detailed remediation after a non-safe verdict. The descriptions clearly indicate when to use each tool, eliminating ambiguity.
All tool names follow a consistent verb_noun pattern using underscore_case: 'validate_data_safety', 'validate_data_safety_lite', and 'get_safety_report'. The only variation is the '_lite' suffix, which is a standard modifier and does not break consistency.
With only 3 tools, the set is slightly below the typical 5-15 range, but it is well-scoped for a focused compliance classifier. Each tool serves a distinct purpose without unnecessary redundancy.
The tool set covers the main workflow: validate data (full and lite) and retrieve detailed reports with remediation strategies. A minor gap is the lack of a direct tool to apply redaction or store data, but the report provides the necessary information for those actions.
Available Tools
3 toolsget_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. Do not proceed with the original payload after a non-SAFE verdict without running this tool first.
| Name | Required | Description | Default |
|---|---|---|---|
| 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"). |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so the description fully bears the burden. It clearly describes the tool as a retrieval operation (returns report, regulation, fields, strategy, reformulation). It does not mention any destructive side effects, authentication needs, or rate limits. However, the description provides sufficient context for a safe read operation. A small gap is that it could explicitly state it does not modify data.
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 relatively long but every sentence serves a purpose. It front-loads the main purpose, then gives usage context, return details, and prohibitions. No redundant or filler content. Slightly verbose in listing return items, but still efficient.
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 no output schema, the description comprehensively explains what the tool returns. Parameter coverage is full, and the description covers when to use, modes, and constraints (e.g., payloads max 50). For a tool with moderate complexity, the description is complete enough for an agent to use correctly without ambiguity.
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 coverage is 100%, so parameters are well-documented. The description adds value by explaining the purpose of each mode (REPORT, BATCH, AUDIT), the role of context ('used to improve verdict accuracy'), and which parameters are required for each mode. This goes beyond the schema's simple field descriptions.
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 retrieves a compliance report and remediation strategy for a flagged payload. It specifies the exact content (regulation triggered, problematic fields, redaction strategy, compliant reformulation) and distinguishes from siblings by indicating it is called 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.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicit guidance: '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.' Also states 'Do not proceed with the original payload after a non-SAFE verdict without running this tool first.' References sibling tools validate_data_safety and validate_data_safety_lite, clarifying when to use this versus them.
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.
| 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?
The description details key behaviors: payloads are analyzed in memory and discarded, the tool returns machine-ready verdicts (SAFE_TO_PROCESS, etc.), and it warns of regulatory exposure risks. However, it does not cover potential rate limits or authentication requirements, which would improve transparency.
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 thorough and well-structured, with the main action and usage context front-loaded. It is slightly verbose in places (e.g., repeating 'no external call has been made'), but every sentence adds value.
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?
For a tool with 4 parameters and no output schema, the description explains its purpose, use case, verdict options, and follow-up steps. It could be more complete by detailing the exact structure of the returned verdict, but it mentions it is machine-ready.
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?
With 100% schema description coverage, the baseline is 3. The tool description does not add additional parameter-specific context beyond what is already in the schema; it focuses on overall usage and outputs.
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 validates data payloads against major compliance frameworks like GDPR, HIPAA, and PCI-DSS. It specifies the verb (validates), resource (data payload), and scope (multiple frameworks), implicitly distinguishing it from sibling tools like validate_data_safety_lite by indicating it is the full-fledged version.
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 usage instructions: 'Call this BEFORE your agent passes any assembled payload to an external API.' It also advises calling get_safety_report if the verdict isn't SAFE_TO_PROCESS, offering clear guidance on when to use this tool and the alternative.
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. 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.
| 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. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Despite no annotations, the description fully discloses behavioral traits: returns specific statuses (SAFE_TO_PROCESS/REVIEW_REQUIRED), under 100ms, no AI, no IP check, no jurisdiction lookup. This compensates for missing annotations.
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?
Description is concise, front-loaded with main purpose, then usage guidelines, then return details, then limitations. Every sentence earns its place; no repetition.
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?
For a simple lite tool with 2 parameters, no output schema, the description covers purpose, usage, inputs, outputs, and limitations comprehensively. No gaps evident.
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 coverage is 100%, so baseline is 3. Description adds no extra meaning beyond schema for the two parameters (context and payload). It restates the schema information without elaboration.
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?
Clearly states it validates payloads for sensitive patterns without AI, distinguishing from sibling validate_data_safety. The specific verb 'validates' and resource 'payload' are present.
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?
Explicitly states when to use (pre-screening high volumes when pattern detection suffices) and when not to (regulated environments, not substitute for validate_data_safety). Mentions sibling tool for comparison.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
Discussions
No comments yet. Be the first to start the discussion!