training_review

Review and approve/reject training pairs. Example: training_review({ pair_id:

SERVER0nmcp SOURCE0nmcp

Medium RISK CLASS

Category Write

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-0nork-0nmcp/training-review.md

WHAT IT DOES

What training_review does on 0nmcp

AI agents use training_review to create or update resources in 0nmcp, usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your 0nmcp environment.

RISK

Why training_review is rated Medium

This tool changes the status/disposition of training pairs (approve or reject), which is a reversible state modification (Write). It does not delete data, execute code, or involve financial transactions. Severity is medium because misuse could corrupt or bias training datasets, but the action appears reversible.

From the tool's definition 'Review and approve/reject training pairs' — modifies the state of training data pairs by approving or rejecting them

Attacks that exploit this kind of access

RECOMMENDED RULE

The rule that runs training_review safely

PolicyLayer is an MCP gateway: it sits between your AI agents and 0nmcp, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For training_review, this is the rule to start with:

training_review Rate-limited

training_review 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": {
    "training_review": {
      "limits": [
        {
          "counter": "training_review_rate",
          "window": "minute",
          "max": 30,
          "scope": "grant"
        }
      ]
    }
  }
}

LIMIT THIS TOOL → Instant setup, no code required.

The button opens the PolicyLayer dashboard: create your workspace, connect 0nmcp, apply this rule, and every training_review call is checked against it from then on.

FAQ

Questions about training_review

What does the training_review tool do? +

Review and approve/reject training pairs. Example: training_review({ pair_id:. It is categorised as a Write tool in the 0nmcp MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.

How do I enforce a policy on training_review? +

Register the 0n MCP server in PolicyLayer and add a rule for training_review: 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 0nmcp. Nothing to install.

What risk level is training_review? +

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

Can I rate-limit training_review? +

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

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

training_review is provided by the 0n MCP server (0nmcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.

KEEP EXPLORING

More on 0n, and thousands of servers like it.

This server

All 218 0n tools →

Across the catalogue

The MCP Attack Database →

Guides

Govern customer-ops agents →Argument validation →Scoped token →