Reject a discovered candidate rule so it will not be promoted into a Blueprint. Use after running discover_patterns when reviewing the candidate rules it produced. Pair with approve_rule: approve high-confidence rules you want enforced, reject the rest. Rejected rules are marked in the discovery ...
Risk signalsHandles credentials or secrets (api_key)
Part of the Governance Platform server.
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AI agents use reject_rule to create or modify resources in Governance Platform. Write operations carry medium risk because an autonomous agent could trigger bulk unintended modifications. Rate limits prevent a single agent session from making hundreds of changes in rapid succession. Argument validation ensures the agent passes expected values.
Without a policy, an AI agent could call reject_rule repeatedly, creating or modifying resources faster than any human could review. PolicyLayer's rate limiting ensures write operations happen at a controlled pace, and argument validation catches malformed or unexpected inputs before they reach Governance Platform.
Write tools can modify data. A rate limit prevents runaway bulk operations from AI agents.
{
"version": "1",
"default": "deny",
"tools": {
"reject_rule": {
"limits": [
{
"counter": "reject_rule_rate",
"window": "minute",
"max": 30,
"scope": "grant"
}
]
}
}
}See the full Governance Platform policy for all 31 tools.
These attack patterns abuse exactly the kind of access reject_rule gives an agent. Each links to the full case and the policy that stops it:
Other write tools across the catalogue. The same approach applies to each: rate-limit and validate the arguments.
Reject a discovered candidate rule so it will not be promoted into a Blueprint. Use after running discover_patterns when reviewing the candidate rules it produced. Pair with approve_rule: approve high-confidence rules you want enforced, reject the rest. Rejected rules are marked in the discovery session and will not appear in subsequent approval calls or be promotable into Blueprints. The action is recorded against the discovery session namespace; it does not affect any existing Blueprint. Use this when: - A discovered rule is mathematically valid but business-irrelevant - The rule has low confidence and you don't want it surfacing again - You want to clean up the candidate list before promoting approvals Args: api_key: GeodesicAI API key (starts with gai_) rule_id: ID of the discovered rule (from discover_patterns results) blueprint: Discovery session namespace (must match the one used for discover_patterns) Returns: status: "rejected" on success rule_id: The rejected rule's ID blueprint: The discovery session namespace. It is categorised as a Write tool in the Governance Platform MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
Register the Governance Platform MCP server in PolicyLayer and add a rule for reject_rule: allow, deny, rate-limit, or require approval. Point your MCP client at the PolicyLayer proxy URL and the rule is enforced on every call, before it reaches Governance Platform. Nothing to install.
reject_rule is a Write tool with medium risk. Write tools should be rate-limited to prevent accidental bulk modifications.
Yes. Add a rate_limit block to the reject_rule rule in your PolicyLayer policy. For example, setting max: 10 and window: 60 limits the tool to 10 calls per minute. Rate limits are tracked per agent session and reset automatically.
Set action: deny in the PolicyLayer policy for reject_rule. The AI agent will receive a policy violation error and cannot call the tool. You can also include a reason field to explain why the tool is blocked.
reject_rule is provided by the Governance Platform MCP server (https://app.geodesiclabs.ai/mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Deterministic rules across all 31 Governance Platform tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.
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