load_module
Load a keyed adventure to explore, from the JSON the betaOSRIC module panel exports (a rooms array/object; apiOSRIC snake_case or ours). Replaces any module in progress.
This record as markdown: /tools/torch-to-crown/load-module.md
What load_module does on Torch-to-Crown
AI agents call load_module to retrieve information from Torch-to-Crown without modifying anything. It is typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
Why load_module is rated Low
Even though load_module only reads data, uncontrolled read access leaks sensitive information and racks up API costs: an agent caught in a retry loop can make thousands of calls a minute without anyone noticing.
Attacks that exploit this kind of access
The rule that runs load_module 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 load_module, this is the rule to start with:
load_module is read-only, so it stays allowed. 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 load_module call is checked against it from then on.
Questions about load_module
Load a keyed adventure to explore, from the JSON the betaOSRIC module panel exports (a rooms array/object; apiOSRIC snake_case or ours). Replaces any module in progress. It is categorised as a Read tool in the Torch-to-Crown MCP Server, which means it retrieves data without modifying state.
Register the Torch-to-Crown MCP server in PolicyLayer and add a rule for load_module: 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.
load_module is a Read tool with low risk. Read-only tools are generally safe to allow by default.
Yes. Add a rate_limit block to the load_module 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 load_module. 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.
load_module 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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