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agent_tool_risk_audit

Statically audits an agent tool definition to detect schema-level risks like over-broad parameters, missing constraints, dangerous defaults, and exfiltration patterns.

Instructions

Statically audit a single agent tool definition for schema-level risks.

Accepts OpenAI function-calling, Anthropic tool-use, MCP tool, or a bare JSON Schema. Reports:

  • over-broad params (bare-string paths/commands/URLs)

  • missing constraints (enum/pattern/min/max/maxLength/maxItems)

  • dangerous defaults (suspicious paths, disabled safeguards)

  • exfil-shape (URL-destination + data-payload in the same tool)

  • ambiguous descriptions vs risky tool names

Args: schema: A tool definition as a dict.

Returns: Structured AuditReport. Pure function, no I/O, no chaining.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
schemaYes
Behavior4/5

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

No annotations are provided, so the description carries full burden. It explicitly states 'Pure function, no I/O, no chaining' and lists input formats and report categories, offering good transparency. Could mention error handling for malformed schemas.

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?

Description is front-loaded with purpose and structured with a list of reports, Args, and Returns. It is moderately concise, though the bullet points are presented as plain text, which is acceptable but slightly less readable.

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 tool's complexity (auditing for schema risks) and absence of output schema, the description provides substantial context: input format, output type, and behaviors checked. It is sufficiently complete for an agent to understand usage.

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

Parameters4/5

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

The schema has no description for the parameter, but the description explains 'schema: A tool definition as a dict', adding meaning beyond the schema's bare 'object' type. It clarifies acceptable formats (OpenAI, Anthropic, etc.).

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 'audit' and the resource 'a single agent tool definition', and lists specific risk categories it checks, distinguishing it from siblings like mcp_server_audit or prompt_injection_audit.

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 provides no guidance on when to use this tool versus alternatives. It does not mention prerequisites, when not to use it, or how it compares to sibling tools.

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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