agent_memory

A other tool on the GadgetHumans API Hub MCP server.

SERVERGadgetHumans API Hub SOURCEpypi:gadgethumans-api-hub-mcp

Low RISK CLASS

Category Other

Parameters 00 required

Recommended Rate-limitedsee the rule below

Registry record Grade F, identity unverified Pull the record →

This record as markdown: /tools/io-github-scotia1973-bot-api-hub/agent-memory.md

WHAT IT DOES

What agent_memory does on GadgetHumans API Hub

AI agents call agent_memory as a supporting operation in GadgetHumans API Hub workflows.

RISK

Why agent_memory is rated Low

The description is entirely empty, making it impossible to determine what this tool does with confidence. The name 'agent_memory' could imply reading, writing, or managing memory/state for an AI agent, but without any description or context, no definitive category can be assigned.

From the tool's definition Tool name is 'agent_memory'; description is empty and uninformative.

Attacks that exploit this kind of access

RECOMMENDED RULE

The rule that runs agent_memory safely

PolicyLayer is an MCP gateway: it sits between your AI agents and GadgetHumans API Hub, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For agent_memory, this is the rule to start with:

agent_memory Rate-limited

agent_memory gets a rate cap, and everything else on the server is denied unless you say otherwise.

View as policy code

policy.json

{
  "version": "1",
  "default": "deny",
  "tools": {
    "agent_memory": {
      "limits": [
        {
          "counter": "agent_memory_rate",
          "window": "minute",
          "max": 60,
          "scope": "grant"
        }
      ]
    }
  }
}

SET A RULE FOR THIS TOOL → Instant setup, no code required.

The button opens the PolicyLayer dashboard: create your workspace, connect GadgetHumans API Hub, apply this rule, and every agent_memory call is checked against it from then on.

FAQ

Questions about agent_memory

What does the agent_memory tool do? +

agent_memory is a other tool on the GadgetHumans API Hub MCP server. It is categorised as a Other tool in the GadgetHumans API Hub MCP Server, which means it performs auxiliary operations.

How do I enforce a policy on agent_memory? +

Register the GadgetHumans API Hub MCP server in PolicyLayer and add a rule for agent_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 GadgetHumans API Hub. Nothing to install.

What risk level is agent_memory? +

agent_memory is a Other tool with low risk. Read-only tools are generally safe to allow by default.

Can I rate-limit agent_memory? +

Yes. Add a rate_limit block to the agent_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 agent_memory completely? +

Set action: deny in the PolicyLayer policy for agent_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 agent_memory? +

agent_memory is provided by the GadgetHumans API Hub MCP server (pypi:gadgethumans-api-hub-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.

KEEP EXPLORING

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