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tosin2013

mcp-adr-analysis-server

by tosin2013
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Hybrid

analyze_content_security

Read-onlyIdempotent

Detect sensitive information in content with AI-powered analysis, and use optional memory integration to learn security patterns for stronger protection.

Instructions

Analyze content for sensitive information using AI-powered detection with optional memory integration for security pattern learning

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
contentYesContent to analyze for sensitive information
contentTypeNoType of content being analyzedgeneral
enhancedModeNoEnable advanced prompting features
userDefinedPatternsNoUser-defined sensitive patterns to detect
knowledgeEnhancementNoEnable Generated Knowledge Prompting for security and privacy expertise
enableMemoryIntegrationNoEnable memory entity storage for security pattern learning and institutional knowledge building

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changedv1.0.0
    • addedInput schema / properties / enableMemoryIntegration
      Added value: +{
+  "default": true,
+  "description": "Enable memory entity storage for security pattern learning and institutional knowledge building",
+  "type": "boolean"
+}
    • addedInput schema / properties / enhancedMode
      Added value: +{
+  "default": true,
+  "description": "Enable advanced prompting features",
+  "type": "boolean"
+}
    • addedInput schema / properties / knowledgeEnhancement
      Added value: +{
+  "default": true,
+  "description": "Enable Generated Knowledge Prompting for security and privacy expertise",
+  "type": "boolean"
+}
  2. First observed

TDQS

A3.6/5.0
Behavior4/5

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

Annotations already declare readOnly and idempotent behavior, and the description adds a useful behavioral trait beyond them: optional memory integration for security pattern learning. It stops short of spelling out side effects such as default-on memory writes or whether learned patterns persist, but it does not contradict the annotations.

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?

A single sentence that front-loads the verb and resource, then gives two useful qualifiers: detection method and memory behavior. There is no filler or redundant restatement of the tool name.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description leaves the result format unstated, which is a significant gap for an analysis tool. It also lacks guidance differentiating it from the security-masking and validation siblings, and does not clarify whether memory integration writes persisted state by default.

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%, so the schema already documents all six parameters. The description only restates the top-level memory integration concept at a high level and adds no format, syntax, or default information beyond what the schema provides.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific verb and resource: 'Analyze content for sensitive information', making the core function immediately identifiable. It clearly contrasts with sibling masking/configuration tools by framing the operation as analysis/detection, though it does not explicitly name an alternative.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The purpose statement implies the primary use case: detect sensitive information in content, which is a real usage signal. However, it gives no explicit guidance on when to prefer this over sibling tools like generate_content_masking or validate_content_masking, nor any when-not-to-use conditions.

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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