Medium Risk

caddy_config_set

Write config at a JSON path. Mode

How to control caddy_config_set ↓

What caddy_config_set does on Caddy

AI agents use caddy_config_set to create or update resources in Caddy — usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your Caddy environment.

Medium Risk

Why caddy_config_set needs a policy

This tool creates or modifies server configuration data reversibly through the Caddy admin API. It falls under Write rather than Execute because it changes configuration state rather than triggering arbitrary operations, and it's reversible (configs can be updated or reverted).

From the tool's definition Tool name 'caddy_config_set' and description 'Write config at a JSON path' explicitly indicate data modification. This writes configuration to a running Caddy web server instance.

Documented attack patterns abuse exactly the kind of access caddy_config_set gives an agent:

How to control caddy_config_set

PolicyLayer is an MCP gateway — it sits between your AI agents and Caddy, and nothing reaches the server without passing your rules. This is the rule we recommend for caddy_config_set:

policy.json
{
  "version": "1",
  "default": "deny",
  "tools": {
    "caddy_config_set": {
      "limits": [
        {
          "counter": "caddy_config_set_rate",
          "window": "minute",
          "max": 30,
          "scope": "grant"
        }
      ]
    }
  }
}

caddy_config_set 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.

  1. Create a free account and register Caddy — nothing to install.
  2. Add this policy — paste it, or build it visually.
  3. Point your MCP client (Claude, Cursor, anything) at your gateway URL.
LIMIT THIS TOOL →

Free to start. No card required.

Related tools and policies

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Questions about caddy_config_set

What does the caddy_config_set tool do? +

Write config at a JSON path. Mode. It is categorised as a Write tool in the Caddy MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.

How do I enforce a policy on caddy_config_set? +

Register the Caddy MCP server in PolicyLayer and add a rule for caddy_config_set: 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 Caddy. Nothing to install.

What risk level is caddy_config_set? +

caddy_config_set is a Write tool with medium risk. Write tools should be rate-limited to prevent accidental bulk modifications.

Can I rate-limit caddy_config_set? +

Yes. Add a rate_limit block to the caddy_config_set 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.

How do I block caddy_config_set completely? +

Set action: deny in the PolicyLayer policy for caddy_config_set. 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.

What MCP server provides caddy_config_set? +

caddy_config_set is provided by the Caddy MCP server (yawlabs/caddy-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.

Enforce policy on every Caddy tool call.

Start from Caddy, add the rest of your stack, and see everything your agents can call. Then put policy on all of it.

Free to start. No card required.

18 Caddy tools catalogued and risk-classified — across an index of 43,000+ MCP servers.

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