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evaluate_batch

Batch evaluate AI agent tool calls against governance policies to check compliance, assess risks, and enforce approval requirements.

Instructions

Evaluate multiple actions at once against the governance policy.

    Each action dict should have: action_type, target,
    and optionally params, description, agent_id.

    Args:
        actions: List of action dicts to evaluate.
    

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
actionsYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure but fails to mention critical batch operation traits: whether evaluation is atomic (all-or-nothing), partial failure handling, audit logging implications, or if the operation is read-only versus creating evaluation records.

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 appropriately compact with three logical parts: purpose statement, input structure requirements, and parameter docstring. No significant redundancy given the schema's lack of descriptions, though 'Args:' section slightly echoes schema.

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

Completeness3/5

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

While the input schema is documented via description text and output schema exists (reducing need for return value description), the description lacks batch-specific operational context such as concurrency limits, transaction boundaries, or error handling patterns that would be expected for a governance evaluation tool.

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?

With 0% schema description coverage, the description compensates effectively by detailing the expected structure of the nested action dictionaries (required fields: action_type, target; optional: params, description, agent_id), adding necessary semantic context missing from the generic 'object' typing in the schema.

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?

The description clearly states the tool evaluates multiple actions against the governance policy, using specific verb 'evaluate' and resource 'actions'. The phrase 'at once' and 'multiple' distinguishes this from the sibling 'evaluate_action' (singular), though it could explicitly name the sibling for clarity.

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?

No explicit guidance is provided on when to use this batch tool versus the singular 'evaluate_action' sibling, or warnings about batch size limits. The description only details input structure without comparing alternatives.

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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