log_completed_session · Pelaris MCP: Risk & Policy

WHAT IT DOES

What log_completed_session does on Pelaris

AI agents use log_completed_session 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_completed_session needs a policy

This tool creates a new workout log entry with associated data (exercises, RPE, feedback, coach notes). It is a data creation/write operation. It is reversible (logs can be deleted via sibling tools like delete_session). Duplicate prevention indicates idempotency but does not change the core write nature. Misuse could result in false workout history but has limited blast radius.

From the tool's definition Log a completed workout retroactively with exercises, RPE, feedback, and coach notes. Prevents duplicate entries automatically.

Attacks that exploit this kind of access

Recommended rule Ready

Verdict

log_completed_session Rate-limited

log_completed_session 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_completed_session": {
      "limits": [
        {
          "counter": "log_completed_session_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_completed_session

What does the log_completed_session tool do? +

Log a completed workout retroactively with exercises, RPE, feedback, and coach notes. Prevents duplicate entries automatically. 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_completed_session? +

Register the Pelaris MCP server in PolicyLayer and add a rule for log_completed_session: 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_completed_session? +

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

Can I rate-limit log_completed_session? +

Yes. Add a rate_limit block to the log_completed_session 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_completed_session completely? +

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

log_completed_session 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.