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Glama

Data Compliance Classifier MCP

by io.github.OjasKord

validate_data_safety_lite

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.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
contextNoOptional: what your agent plans to do with this data.
payloadYesThe data payload to screen for sensitive patterns.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
verdictYes
checked_atYes
_disclaimerYes
agent_actionYes
analysis_typeNo
patterns_detectedNo
sensitivity_levelNo

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changed
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "additionalProperties": true,
+  "properties": {
+    "_disclaimer": {
+      "type": "string"
+    },
+    "agent_action": {
+      "type": "string"
+    },
+    "analysis_type": {
+      "type": "string"
+    },
+    "checked_at": {
+      "format": "date-time",
+      "type": "string"
+    },
+    "patterns_detected": {
+      "items": {
+        "type": "string"
+      },
+      "type": "array"
+    },
+    "sensitivity_level": {
+      "enum": [
+        "PUBLIC",
+        "INTERNAL",
+        "CONFIDENTIAL"
+      ],
+      "type": "string"
+    },
+    "verdict": {
+      "enum": [
+        "SAFE_TO_PROCESS",
+        "REVIEW_REQUIRED"
+      ],
+      "type": "string"
+    }
+  },
+  "required": [
+    "verdict",
+    "agent_action",
+    "checked_at",
+    "_disclaimer"
+  ],
+  "type": "object"
+}
  2. Added

TDQS

A4.7/5.0
Behavior5/5

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

No annotations are provided, so the description carries the full burden. It discloses performance ('under 100ms'), scope ('no AI, no IP check, no jurisdiction lookup'), and limitations (warns about contextual PII and non-standard credential formats slipping through). This is exemplary transparency that goes well beyond the schema.

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

Conciseness5/5

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

The description is efficiently structured: it opens with the core purpose, then usage, then limitations, with no wasted words. Every sentence conveys essential information, and the warnings about misuse are concise but impactful.

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?

The description fully covers the tool's purpose, usage, alternatives, performance characteristics, and limitations. Given the output schema exists and the description is this rich, the agent has everything needed to decide when and how to invoke the tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100% with clear descriptions for both 'payload' and 'context'. The tool description does not add new parameter-level semantics beyond what the schema already provides, but it neither contradicts nor confuses them, so baseline 3 is appropriate.

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 payload for sensitive patterns without AI classification', specifying the verb, resource, and method. It distinguishes itself from the sibling 'validate_data_safety' by emphasizing it is a fast, pattern-only filter, making the purpose unambiguous.

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?

Provides explicit usage guidance: 'Call this BEFORE pre-screening high-volume payloads' and 'Use to filter large batches before selectively running validate_data_safety on flagged payloads'. It also gives a clear exclusion: 'Do not use as a substitute for validate_data_safety before storing or transmitting data in regulated environments', naming the alternative and when to avoid this tool.

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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TDQS

A4.6/5.0
Disambiguation5/5

Each tool has a distinct role: validate_data_safety performs full AI classification, validate_data_safety_lite is a fast pattern-only pre-filter, and get_safety_report provides actionable remediation after a non-safe verdict. The clear workflow descriptions eliminate overlap.

Naming Consistency5/5

All tools use a verb_noun pattern with snake_case (get_safety_report, validate_data_safety, validate_data_safety_lite). The shared prefix for the two validation tools is consistent, and the lite suffix clearly indicates the lightweight variant.

Tool Count5/5

Three tools is well-scoped for the server's purpose: a primary validator, a fast pre-filter, and a follow-up report generator. There is no unnecessary bloat or missing core functionality.

Completeness5/5

The server covers the full compliance workflow: validate, optionally pre-filter with lite, and retrieve detailed reports when needed. The descriptions indicate support for multiple frameworks and clear redaction strategies, leaving no obvious dead ends in the primary use case.

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