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dingdawg

dingdawg-governance

by dingdawg

govern_action

Validate AI agent actions with capability checks and policy evaluation, then produce a verifiable receipt for audit and rollback.

Instructions

Govern any AI agent action. Performs capability check + policy evaluation + generates a governance receipt. Returns a receipt proving the action was governed. When API key is set, uses cloud API with local fallback.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
agent_idYesIdentifier for the AI agent performing the action
action_typeYesType of action (e.g., 'send_email', 'make_purchase', 'modify_data', 'api_call')
action_descriptionYesHuman-readable description of what the agent is about to do
target_resourceNoThe resource being acted upon (e.g., 'user_database', 'email_server', 'payment_api')
risk_tierNoSelf-assessed risk level of this action
contextNoAdditional context key-value pairs for policy evaluation
Behavior4/5

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

Discloses key behaviors: capability check, policy evaluation, receipt generation, and cloud fallback when API key is set. No annotations exist, so description carries the full burden; it adequately informs about functionality without side-effect warnings.

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?

Two sentences, front-loaded with primary purpose followed by key behavioral detail (fallback mechanism). Every sentence adds value, no redundancy.

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?

Explains the return value (receipt) and process. Lacks detail on error conditions or policy failure handling. No output schema, but description suffices for basic understanding.

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 coverage is 100%, so baseline is 3. The description adds no extra parameter details beyond what the schema provides, earning no bonus.

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?

Description clearly states the tool governs any AI agent action, performing capability checks, policy evaluation, and generating a governance receipt. It distinguishes from sibling tools (audit_trail, compliance_check) by focusing on proactive governance with a receipt output.

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

Usage Guidelines3/5

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

Implies the tool is used for governing AI actions but does not explicitly contrast with siblings or provide when-not-to-use guidance. No alternative tools are suggested for different scenarios.

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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