Ingest raw training material from files, memory, code, or text. Stores in training_sources table for later pair generation. Sources: memory files, .js/.ts code, .md docs, raw text, API patterns. Example: training_ingest({ source_type:
Part of the 0nmcp server.
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AI agents call training_ingest to retrieve information from 0nmcp without modifying any data. This is common in research, monitoring, and reporting workflows where the agent needs context before taking action. Because read operations don't change state, they are generally safe to allow without restrictions -- but you may still want rate limits to control API costs.
Even though training_ingest only reads data, uncontrolled read access can leak sensitive information or rack up API costs. An agent caught in a retry loop could make thousands of calls per minute. A rate limit gives you a safety net without blocking legitimate use.
Read-only tools are safe to allow by default. No rate limit needed unless you want to control costs.
{
"version": "1",
"default": "deny",
"tools": {
"training_ingest": {}
}
}See the full 0nmcp policy for all 407 tools.
These attack patterns abuse exactly the kind of access training_ingest gives an agent. Each links to the full case and the policy that stops it:
Other read tools across the catalogue. The same approach applies to each: allow, with a rate cap to control cost.
Ingest raw training material from files, memory, code, or text. Stores in training_sources table for later pair generation. Sources: memory files, .js/.ts code, .md docs, raw text, API patterns. Example: training_ingest({ source_type:. It is categorised as a Read tool in the 0nmcp MCP Server, which means it retrieves data without modifying state.
Register the 0n MCP server in PolicyLayer and add a rule for training_ingest: 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_ingest is a Read tool with low risk. Read-only tools are generally safe to allow by default.
Yes. Add a rate_limit block to the training_ingest 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_ingest. 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_ingest 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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