training_dataset
Create or manage training datasets — named collections of pairs. Example: training_dataset({ action:
This record as markdown: /tools/io-github-0nork-0nmcp/training-dataset.md
What training_dataset does on 0nmcp
AI agents use training_dataset 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.
Why training_dataset is rated Medium
The tool performs reversible write operations (create, manage) on training data collections. It does not delete (Destructive), execute arbitrary code (Execute), or move money (Financial). The 'medium' severity reflects that misuse could corrupt or poison training datasets, but the impact is bounded to that dataset scope and operations are reversible via subsequent updates or deletion by proper means.
From the tool's definition Tool description states 'Create or manage training datasets' — explicitly names data creation and management operations.
Attacks that exploit this kind of access
The rule that runs training_dataset 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_dataset, this is the rule to start with:
training_dataset 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.
The button opens the PolicyLayer dashboard: create your workspace, connect 0nmcp, apply this rule, and every training_dataset call is checked against it from then on.
Questions about training_dataset
Create or manage training datasets — named collections of pairs. Example: training_dataset({ action:. 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.
Register the 0n MCP server in PolicyLayer and add a rule for training_dataset: 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.
training_dataset is a Write tool with medium risk. Write tools should be rate-limited to prevent accidental bulk modifications.
Yes. Add a rate_limit block to the training_dataset 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.
Set action: deny in the PolicyLayer policy for training_dataset. 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.
training_dataset 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.
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