log_workout · Pelaris MCP: Risk & Policy

WHAT IT DOES

What log_workout does on Pelaris

AI agents use log_workout to create or update resources in Pelaris — usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your Pelaris environment.

WHY IT NEEDS A POLICY

Why log_workout needs a policy

This tool creates a new workout log entry in the system. It writes data (exercises, RPE, subjective feelings) to persistent storage. It is reversible — the sibling tools delete_session/delete_sessions suggest records can be removed. No code execution, financial transactions, or irreversible destruction is involved.

From the tool's definition "Record a completed workout with exercises, RPE, and how you felt. Duplicate entries are automatically prevented."

Attacks that exploit this kind of access

Recommended rule Ready

Verdict

log_workout Rate-limited

log_workout 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": {
    "log_workout": {
      "limits": [
        {
          "counter": "log_workout_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 Pelaris, so you can run agents against it safely.

FAQ

Questions about log_workout

What does the log_workout tool do? +

Record a completed workout with exercises, RPE, and how you felt. Duplicate entries are automatically prevented. It is categorised as a Write tool in the Pelaris MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.

How do I enforce a policy on log_workout? +

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

What risk level is log_workout? +

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

Can I rate-limit log_workout? +

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

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

log_workout is provided by the Pelaris MCP server (thedonk/pelaris-mcp-server). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.