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check_agent_security

Scan AI agent installations for security vulnerabilities by checking configuration and skills for malicious patterns, returning severity and fix hints.

Instructions

Scan an AI agent installation for security issues. Checks agent configuration (gateway binding, authentication, sandbox, API keys in plaintext, DM policy, tool permissions, SSRF protection, file permissions, log redaction) and installed skills for malicious patterns (reverse shells, credential theft, prompt injection, toxic data flows). Returns findings with severity levels and fix hints. Use when auditing an agent's security posture or before deploying an agent to production.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
agent_dirNoPath to agent config directory. Defaults to ~/.openclaw if not specified.
scan_skillsNoInclude skill scanning for malicious patterns (default: true)
verify_pinsNoVerify pinned skills for rug pull detection (default: false)
policyNoScan policy preset (default: balanced)
Behavior4/5

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

Without annotations, the description adequately discloses the tool's behavior: it scans configuration and skills, and returns findings with severity and fix hints. It implies a read-only operation, though explicitly stating non-destructiveness would improve transparency.

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 concise: two sentences for the main functionality and one for usage. Every sentence provides value, no redundancy, and the structure is front-loaded with the core purpose.

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 no output schema, the description covers the return format (findings with severity and fix hints). It adequately describes the tool's scope and outcome, though it could mention the sibling tool to avoid confusion.

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 each parameter well. The description adds overall context but does not enhance parameter semantics beyond what the schema provides, meeting the baseline.

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 verb 'scan' and the resource 'AI agent installation'. It lists specific security checks on configuration and skills, distinguishing it from the sibling tool 'scan_mcp_server' which likely scans MCP servers instead.

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

Usage Guidelines4/5

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

Explicit usage guidance is provided: 'Use when auditing an agent's security posture or before deploying an agent to production.' This gives clear context but does not mention when not to use or alternative tools, such as the sibling for MCP servers.

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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