Medium Risk

save_checkpoint

App-only: save shapes to a checkpoint (from user edits). shapesJson and assetsJson must be JSON array strings.

Part of the Tldraw server.

save_checkpoint can modify Tldraw 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 save_checkpoint to create or modify resources in Tldraw. 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 save_checkpoint 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 Tldraw.

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

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

See the full Tldraw policy for all 6 tools.

Get this rule live on your own Tldraw 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 save_checkpoint 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 save_checkpoint 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 save_checkpoint tool do? +

App-only: save shapes to a checkpoint (from user edits). shapesJson and assetsJson must be JSON array strings.. It is categorised as a Write tool in the Tldraw MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.

How do I enforce a policy on save_checkpoint? +

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

What risk level is save_checkpoint? +

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

Can I rate-limit save_checkpoint? +

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

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

save_checkpoint is provided by the Tldraw MCP server (https://tldraw-mcp-app.tldraw.workers.dev/mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.

Enforce policy on every Tldraw tool call.

Deterministic rules across all 6 Tldraw tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.

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4,600+ MCP servers and 31,000+ tools scanned and risk-classified.

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