training_feed
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:
This record as markdown: /tools/io-github-0nork-0nmcp/training-feed.md
What training_feed does on 0nmcp
AI agents invoke training_feed 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 training_feed is rated High
The tool actively manages and triggers continuous data ingestion pipelines from multiple external sources, which constitutes an external operation whose effects depend on arguments (the 'action' parameter). It is not a simple read because it manages a feed (implying state changes and ongoing processes), but the description is incomplete (truncated example), lowering confidence.
From the tool's definition 'Manage the 0nAI training feed — continuous data ingestion from verified public sources' and 'Fetches from ${FEED_SOURCES.length} sources'
Attacks that exploit this kind of access
The rule that runs training_feed 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_feed, this is the rule to start with:
training_feed 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 training_feed call is checked against it from then on.
Questions about training_feed
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 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 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 Execute tool with high risk. Execute tools should be rate-limited and have argument validation enabled.
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.
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