Medium Risk

propose_edit

Propose an edit to a quote. Requires authentication. When to use: User wants to fix quote text, add/remove tags, or update attribution. Trusted users may have edits auto-activated; others queue for review. Examples: - propose_edit(edit_type="text", quote_ref="abc123", new_text="Corrected spelling...

Part of the Quotewise server.

propose_edit can modify Quotewise 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 propose_edit to create or modify resources in Quotewise. 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 propose_edit 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 Quotewise.

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

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

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

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Every attack above starts with a tool call. PolicyLayer checks each one against your policy first, so propose_edit 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 propose_edit tool do? +

Propose an edit to a quote. Requires authentication. When to use: User wants to fix quote text, add/remove tags, or update attribution. Trusted users may have edits auto-activated; others queue for review. Examples: - propose_edit(edit_type="text", quote_ref="abc123", new_text="Corrected spelling") - fix typo - propose_edit(edit_type="tag_add", quote_ref="abc123", tag_text="philosophy") - add tag - propose_edit(edit_type="attribution", quote_ref="abc123", disputed=True) - mark disputed. It is categorised as a Write tool in the Quotewise MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.

How do I enforce a policy on propose_edit? +

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

What risk level is propose_edit? +

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

Can I rate-limit propose_edit? +

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

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

propose_edit is provided by the Quotewise MCP server (https://mcp.quotewise.io/). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.

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