brain_train
Run scenario-based training on a brain. Executes each scenario against the brain
This record as markdown: /tools/io-github-0nork-0nmcp/brain-train.md
What brain_train does on 0nmcp
AI agents invoke brain_train to trigger actions in 0nmcp. What it does depends on the arguments the agent supplies, and its effects often reach beyond the immediate call: builds kicked off, notifications sent, workflows started.
Why brain_train is rated High
The tool runs scenarios against a 'brain' (likely a neural network, LLM, or similar AI model), which constitutes executing operations whose effects depend on the scenario arguments. This is not a simple read (no retrieval language), and the execution of arbitrary scenarios against a system poses a medium-to-high risk if misused (resource exhaustion, model poisoning, unintended outputs).
From the tool's definition Tool description states 'Executes each scenario against the brain' — the verb 'executes' indicates active computation or code execution against a target system.
Attacks that exploit this kind of access
The rule that runs brain_train 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 brain_train, this is the rule to start with:
brain_train stays usable, but rate-capped: a runaway agent can't fire it dozens of times 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 brain_train call is checked against it from then on.
Questions about brain_train
Run scenario-based training on a brain. Executes each scenario against the brain. It is categorised as a Execute tool in the 0nmcp MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the 0n MCP server in PolicyLayer and add a rule for brain_train: 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.
brain_train is a Execute tool with high risk. Execute tools should be rate-limited and have argument validation enabled.
Yes. Add a rate_limit block to the brain_train 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 brain_train. 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.
brain_train 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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