Manage the 0nAI training feed — continuous data ingestion from verified public sources. Fetches from ${FEED_SOURCES.length} sources: Hacker News, arXiv, Dev.to, GitHub, npm, CoinGecko, Wikipedia. Example: training_feed({ action:
Part of the 0nmcp server.
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AI agents use training_feed to create or modify resources in 0nmcp. Write operations carry medium risk because an autonomous agent could trigger bulk unintended modifications. Rate limits prevent a single agent session from making hundreds of changes in rapid succession. Argument validation ensures the agent passes expected values.
Without a policy, an AI agent could call training_feed repeatedly, creating or modifying resources faster than any human could review. PolicyLayer's rate limiting ensures write operations happen at a controlled pace, and argument validation catches malformed or unexpected inputs before they reach 0nmcp.
Write tools can modify data. A rate limit prevents runaway bulk operations from AI agents.
{
"version": "1",
"default": "deny",
"tools": {
"training_feed": {
"limits": [
{
"counter": "training_feed_rate",
"window": "minute",
"max": 30,
"scope": "grant"
}
]
}
}
}See the full 0nmcp policy for all 407 tools.
These attack patterns abuse exactly the kind of access training_feed gives an agent. Each links to the full case and the policy that stops it:
Other write tools across the catalogue. The same approach applies to each: rate-limit and validate the arguments.
Manage the 0nAI training feed — continuous data ingestion from verified public sources. Fetches from ${FEED_SOURCES.length} sources: Hacker News, arXiv, Dev.to, GitHub, npm, CoinGecko, Wikipedia. Example: training_feed({ 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_feed: 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_feed 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_feed 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_feed. 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_feed 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.
Deterministic rules across all 407 0nmcp tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.
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