save_memory · Dual MCP Server for IoT & Memory MCP

WHAT IT DOES

What save_memory does on Dual MCP Server for IoT & Memory

AI agents use save_memory to create or update resources in Dual MCP Server for IoT & Memory — usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your Dual MCP Server for IoT & Memory environment.

WHY IT NEEDS A POLICY

Why save_memory needs a policy

The tool creates or modifies data in a persistent memory storage system, which is a reversible write operation. Severity is medium because misuse could pollute the memory store or create confusion, but data can typically be corrected or deleted. Confidence is slightly reduced due to empty tool description, though the name and server context strongly suggest a write operation.

From the tool's definition Tool name 'save_memory' indicates data creation/modification. Server description mentions 'managing persistent memory storage' and 'semantic search capabilities,' confirming this tool writes to a memory system.

Attacks that exploit this kind of access

Recommended rule Ready

Verdict

save_memory Rate-limited

save_memory 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.

View as policy code

policy.json

{
  "version": "1",
  "default": "deny",
  "tools": {
    "save_memory": {
      "limits": [
        {
          "counter": "save_memory_rate",
          "window": "minute",
          "max": 30,
          "scope": "grant"
        }
      ]
    }
  }
}

LIMIT THIS TOOL →

Instant setup, no code required.

PolicyLayer is an MCP gateway: every tool call is checked against your rule before it reaches Dual MCP Server for IoT & Memory, so you can run agents against it safely.

FAQ

Questions about save_memory

What does the save_memory tool do? +

save_memory. It is categorised as a Write tool in the Dual MCP Server for IoT & Memory 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_memory? +

Register the Dual MCP Server for IoT & Memory MCP server in PolicyLayer and add a rule for save_memory: 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 Dual MCP Server for IoT & Memory. Nothing to install.

What risk level is save_memory? +

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

Can I rate-limit save_memory? +

Yes. Add a rate_limit block to the save_memory 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_memory completely? +

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

save_memory is provided by the Dual MCP Server for IoT & Memory MCP server (jordy33/iot_mcp_server). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.