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dashclaw_guard

Evaluate each risky action against governance policies before execution, returning allow, warn, block, or require_approval to prevent unauthorized changes.

Instructions

Evaluate DashClaw governance policies before taking a risky action. Call this BEFORE any action that modifies external systems, deploys code, sends messages, or touches production data. Returns a decision: "allow" (proceed), "warn" (proceed with caution), "block" (stop), or "require_approval" (wait for human in Mission Control). If the decision is "block", do NOT proceed with the action.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
targetNoPrimary file path, URL, or resource the action touches (lets protected-path policies match)
contentNoOutbound content excerpt (file content, message body) so secret-scan and content policies can evaluate it
agent_idNoFallback identity when no server-level agent id is configured (the configured id wins)
tool_nameNoName of the tool that will perform the action (e.g., Write, Bash, send_email)
reversibleNoWhether the action can be undone
risk_scoreYesEstimated risk 0-100. Use 70+ for production systems.
action_typeYesCategory of action (e.g., deploy, send_email, database_write, api_call)
write_pathsNoFile paths the action will write or modify (protected-path policy matching)
declared_goalYesWhat you intend to do, in plain language
systems_touchedNoSystems affected (e.g., production, database, email)
Behavior4/5

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

The description fully discloses the return decision types and their implications, including the critical directive to not proceed on 'block'. No annotations are present, so the description carries the burden well, though it omits details on side effects like logging or authorization requirements.

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 concise (4 sentences) and front-loaded with the core purpose. It could benefit from bullet points for readability, but it efficiently conveys essential information without waste.

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?

Given the complexity (10 parameters, no output schema), the description covers basic usage and behavior. However, it lacks details about the decision structure (e.g., additional context like reasons) and does not explain policy evaluation mechanisms, leaving some gaps for a guard tool.

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?

The input schema has 100% coverage with descriptions for all 10 parameters. The description adds no additional parameter semantics beyond the schema, so the baseline score of 3 is appropriate.

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 governance policies for risky actions, listing specific return values. However, it does not explicitly differentiate from siblings like 'check_policy' or 'simulate_action', which could overlap in purpose.

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?

The description explicitly instructs to call this 'BEFORE' risky actions, lists concrete examples of such actions, and provides directives for each possible return value. No alternatives or when-not-to-use scenarios are mentioned, but the context is clear.

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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