evaluate_action

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

No annotations provided, so description carries burden. It discloses return decisions and parameter roles but lacks details on side effects, authentication, rate limits, or error handling. The description adds moderate transparency beyond the schema.

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

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

The description is front-loaded with the main purpose, followed by parameter explanations. It is reasonably concise without wasting words, though the bullet-style list could be more compact.

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

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

With an output schema existing, return values are not fully described, but the description mentions three decisions. It lacks examples or constraints on parameter values. For a tool with 5 params and 0% schema coverage, the description is adequate but not deeply complete.

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

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

Schema description coverage is 0%, but the description compensates by explaining each parameter's meaning (action_type, target, params, description, agent_id). While not exhaustive, it provides sufficient context for an agent to understand required inputs.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool evaluates an AI agent action against governance policy and returns a decision (auto, approve, block). It uses a specific verb-resource combination and distinguishes itself from siblings by focusing on individual action evaluation.

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

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

No guidance on when to use this tool versus siblings like check_risk or evaluate_batch. No explicit when-to-use or when-not-to-use conditions are provided, leaving the agent to infer context independently.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.