Medium Risk

contentrain_model_save

Create or update a model definition. Changes are auto-committed to git — do NOT manually edit .contentrain/ files after calling this tool.

Part of the Contentrain server.

contentrain_model_save can modify Contentrain data, with no limits today. PolicyLayer puts allow, deny, and rate-limit rules on every call. Live in minutes.

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AI agents use contentrain_model_save to create or modify resources in Contentrain. 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 contentrain_model_save 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 Contentrain.

Write tools can modify data. A rate limit prevents runaway bulk operations from AI agents.

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

See the full Contentrain policy for all 19 tools.

Get this rule live on your own Contentrain server in minutes. PolicyLayer enforces it on every call, before it runs.

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These attack patterns abuse exactly the kind of access contentrain_model_save gives an agent. Each links to the full case and the policy that stops it:

Browse the full MCP Attack Database →

Every attack above starts with a tool call. PolicyLayer checks each one against your policy first, so contentrain_model_save only ever does what you allow.

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Other write tools across the catalogue. The same approach applies to each: rate-limit and validate the arguments.

What does the contentrain_model_save tool do? +

Create or update a model definition. Changes are auto-committed to git — do NOT manually edit .contentrain/ files after calling this tool.. It is categorised as a Write tool in the Contentrain MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.

How do I enforce a policy on contentrain_model_save? +

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

What risk level is contentrain_model_save? +

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

Can I rate-limit contentrain_model_save? +

Yes. Add a rate_limit block to the contentrain_model_save 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 contentrain_model_save completely? +

Set action: deny in the PolicyLayer policy for contentrain_model_save. 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 contentrain_model_save? +

contentrain_model_save is provided by the Contentrain MCP server (@contentrain/mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.

Enforce policy on every Contentrain tool call.

Deterministic rules across all 19 Contentrain tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.

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