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scan_for_vulnerabilities

Scan source code for security vulnerabilities like SQL injection, XSS, and hardcoded secrets using taint-flow analysis and CVSS-based scoring.

Instructions

Scan code content for security vulnerabilities (SAST analysis).

Uses a 55-rule engine with taint-flow simulation and CVSS-inspired scoring. Detects hardcoded secrets, SQL injection, path traversal, command injection, insecure cryptography, unsafe deserialization, XSS, and authentication misconfigurations.

Args: content: The source code to scan. source: File path / identifier (used for language detection and confidence scoring). E.g. "auth/login.py".

Returns JSON with: - findings: [{rule_id, cwe, severity, line_number, description, fix, confidence, taint_flow}] - risk_score: CVSS-inspired aggregate [0.0, 10.0] - critical_count, high_count, medium_count, low_count - top_fix: most impactful remediation action

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sourceNounknown
contentYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

With no annotations, the description carries full burden. It explains the tool uses a 55-rule engine with taint-flow simulation and CVSS scoring, and details the output. However, it omits aspects like permissions required, potential false positives, or performance impact, so transparency is adequate but not exhaustive.

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

Conciseness4/5

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

The description is well-structured with a concise intro, a bullet list of detected vulnerabilities, and clear sections for args and returns. It is front-loaded with the core function. Slightly verbose due to the vulnerability list, but still efficient.

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

Completeness4/5

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

Given the lack of annotations and schema descriptions, the description compensates by fully detailing input parameters and output format. It provides enough context for a typical SAST use case. Missing usage guidelines and potential limitations reduce completeness slightly.

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

Parameters5/5

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

Schema coverage is 0%, but the 'Args' section provides full descriptions for both parameters: 'content' is source code, 'source' is a file path used for language detection. This adds critical context beyond the bare schema (type and default), enabling correct parameter usage.

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 'Scan code content for security vulnerabilities (SAST analysis)' and enumerates specific vulnerability types (hardcoded secrets, SQL injection, etc.), making the tool's purpose unambiguous and distinct from potential siblings like security_report or security_scan.

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 does not explicitly state when to use this tool versus alternatives (e.g., security_scan). No conditions or exclusions are provided, leaving the agent without guidance on tool selection among related siblings.

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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