This record as markdown: /tools/torch-to-crown/declare-action.md
What declare_action does on Torch-to-Crown
AI agents use declare_action to create or update resources in Torch-to-Crown, usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your Torch-to-Crown environment.
Why declare_action is rated Medium
An AI agent can call declare_action faster than any human can review: one bad instruction and it creates or modifies resources in Torch-to-Crown by the hundred, each call as confident as the last.
Attacks that exploit this kind of access
The rule that runs declare_action safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Torch-to-Crown, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For declare_action, this is the rule to start with:
declare_action stays usable, but capped: an agent stuck in a loop can't make hundreds of changes a minute. Everything else on the server is denied unless you say otherwise.
The button opens the PolicyLayer dashboard: create your workspace, connect Torch-to-Crown, apply this rule, and every declare_action call is checked against it from then on.
Questions about declare_action
Commit a combatant. It is categorised as a Write tool in the Torch-to-Crown MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
Register the Torch-to-Crown MCP server in PolicyLayer and add a rule for declare_action: 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 Torch-to-Crown. Nothing to install.
declare_action 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 declare_action 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 declare_action. 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.
declare_action is provided by the Torch-to-Crown MCP server (Diogenes187/Torch-to-Crown). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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