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@guardbee/mcp-security-proxy

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ai_code_scanner__scan_text

Scan code and text for insecure AI/LLM patterns. Detects exposed keys, unsafe output handling, excessive agent permissions, PII leaks, and prompt injection.

Instructions

Scan a text string or code snippet for insecure AI/LLM integration patterns (client-exposed keys, unsafe output handling, excessive agent tool permissions, PII sent to a model, prompt injection surface).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
labelNoOptional label shown in findings (e.g. filename)
contentYesThe code content to scan

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.1.1

TDQS

C2.9/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full behavioral burden. It does not state that the operation is read-only, whether permissions are required, what the findings look like, or any rate limits—it only implies a read operation via "Scan."

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 a single front-loaded sentence with the verb first and a compact parenthetical list of pattern types. Every phrase contributes to defining the scan scope with no wasted words.

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 annotations and no output schema, the description should ideally disclose that the tool is read-only and explain the shape of returned findings. It only lists pattern categories, leaving both safety behavior and return format undocumented for an agent that cannot rely on structured fields.

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 both parameters (content, label) are fully documented in the schema. The description adds no parameter syntax, constraints, or examples, making the baseline score of 3 appropriate.

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?

States a specific verb ("Scan") and resource ("text string or code snippet") plus a clear scope with five named pattern categories. This distinguishes it from ai_code_scanner__scan_file and scan_directory by input type, but it does not explicitly differentiate from the many other *_scan_text siblings (e.g., a2a_auditor__scan_text, secret_scanner__scan_text) or name 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?

The description gives no explicit when-to-use, when-not-to-use, or alternative-tool guidance. The only usage cue is the implied distinction of scanning a text string rather than a file, which is not enough to route an agent among the many sibling scanners.

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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