Medium Risk

approve_suggestion

Approve a pending behavioral suggestion, promoting it to permanent knowledge.

Part of the Total Recall server.

approve_suggestion can modify Total Recall 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 approve_suggestion to create or modify resources in Total Recall. 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 approve_suggestion 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 Total Recall.

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

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

See the full Total Recall policy for all 30 tools.

Get this rule live on your own Total Recall 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 approve_suggestion 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 approve_suggestion 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 approve_suggestion tool do? +

Approve a pending behavioral suggestion, promoting it to permanent knowledge.. It is categorised as a Write tool in the Total Recall MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.

How do I enforce a policy on approve_suggestion? +

Register the Total Recall MCP server in PolicyLayer and add a rule for approve_suggestion: 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 Total Recall. Nothing to install.

What risk level is approve_suggestion? +

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

Can I rate-limit approve_suggestion? +

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

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

approve_suggestion is provided by the Total Recall MCP server (@avi-total-recall/total-recall). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.

Enforce policy on every Total Recall tool call.

Deterministic rules across all 30 Total Recall 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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