user_said_i_did_a_good_job

WHAT IT DOES

What user_said_i_did_a_good_job does on TOOT (Train of Operadic Thought) MCP

AI agents use user_said_i_did_a_good_job to create or update resources in TOOT (Train of Operadic Thought) MCP — usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your TOOT (Train of Operadic Thought) MCP environment.

WHY IT NEEDS A POLICY

Why user_said_i_did_a_good_job needs a policy

This tool creates new records or modifies existing feedback data in the TOOT system's persistent store. It is reversible (records can be corrected or deleted), so it does not qualify as Destructive. It does not execute external commands, trigger financial transactions, or merely read data.

From the tool's definition Tool name and description indicate it 'Record[s] positive feedback and successful patterns' — a write operation that creates or modifies feedback records in a reinforcement learning system.

Attacks that exploit this kind of access

Recommended rule Ready

Verdict

user_said_i_did_a_good_job Rate-limited

user_said_i_did_a_good_job 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": {
    "user_said_i_did_a_good_job": {
      "limits": [
        {
          "counter": "user_said_i_did_a_good_job_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 TOOT (Train of Operadic Thought) MCP, so you can run agents against it safely.

FAQ

Questions about user_said_i_did_a_good_job

What does the user_said_i_did_a_good_job tool do? +

Record positive feedback and successful patterns for reinforcement learning. It is categorised as a Write tool in the TOOT (Train of Operadic Thought) MCP MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.

How do I enforce a policy on user_said_i_did_a_good_job? +

Register the TOOT (Train of Operadic Thought) MCP server in PolicyLayer and add a rule for user_said_i_did_a_good_job: 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 TOOT (Train of Operadic Thought) MCP. Nothing to install.

What risk level is user_said_i_did_a_good_job? +

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

Can I rate-limit user_said_i_did_a_good_job? +

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

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

user_said_i_did_a_good_job is provided by the TOOT (Train of Operadic Thought) MCP server (sancovp/toot-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.