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hawklithm

sensitive-info-mcp

by hawklithm

scan_report

Generates a Markdown scan report for sensitive information detection, with optional AI detection and configurable masking strategies.

Instructions

生成完整的 Markdown 扫描报告

Args: text: 待扫描文本 enable_ai: 是否启用 AI 检测 mask_strategy: 脱敏策略

Returns: Markdown 格式的扫描报告

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYes
enable_aiNo
mask_strategyNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior2/5

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

No annotations are provided, so the description must disclose behavioral traits. It does not mention whether the tool is read-only, has side effects, requires authentication, or any rate limits. It only says it generates a report, which implies a nondestructive operation, but this is not explicit. Critical behavioral context is missing.

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 highly concise: a single main sentence followed by a structured Args/Returns list. Every element contributes to understanding the tool. No redundant or unnecessary words. It is front-loaded and well-organized.

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

Completeness3/5

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

The description covers the basic purpose and parameters, and an output schema exists to document return values. However, it lacks clarity on how this tool differs from siblings like scan_text, and it omits details about the mask_strategy parameter's possible values. For a tool with moderate complexity, it is adequate but not fully complete.

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?

The description adds moderate meaning to the parameters: 'text' is described as '待扫描文本' (text to be scanned), 'enable_ai' as '是否启用 AI 检测' (whether to enable AI detection), and 'mask_strategy' as '脱敏策略' (masking strategy). This goes beyond the schema which only provides names and types. However, it does not specify allowed values for mask_strategy or the effect of enable_ai, leaving gaps.

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 clearly states the tool generates a complete Markdown scan report, which is a specific verb-resource combination. However, it does not explicitly differentiate from sibling tools like scan_file or scan_text, which likely produce different outputs (e.g., structured data). The purpose is clear but not distinguished from alternatives.

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

Usage Guidelines2/5

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

No guidance is provided on when to use this tool versus siblings such as scan_file, scan_text, or mask_text. There is no mention of prerequisites, context, or when not to use this tool. The description only states what it does, leaving the agent to infer usage independently.

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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