Export vault beliefs as JSONL training data for LLM finetuning. Generates instruction-following pairs from compiled beliefs: question about entity → belief body as answer. Filters out stale and low-confidence beliefs. Output is OpenAI-compatible JSONL. Uses PRISM scoring dimensions for quality-we...
AI agents use export_training_data to create or update resources in Entroly Context Engine — usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your Entroly Context Engine environment.
The tool creates and writes a new artifact (JSONL training data file) to a specified output path. This is a Write operation—it produces new data persistently but does not delete, destroy, or irreversibly modify existing data.
From the tool's definition Tool description explicitly states it 'Export vault beliefs as JSONL training data' and 'Generates instruction-following pairs from compiled beliefs' with an 'output_path' argument to 'write JSONL file'.
Documented attack patterns abuse exactly the kind of access export_training_data gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and Entroly Context Engine, and nothing reaches the server without passing your rules. This is the rule we recommend for export_training_data:
{
"version": "1",
"default": "deny",
"tools": {
"export_training_data": {
"limits": [
{
"counter": "export_training_data_rate",
"window": "minute",
"max": 30,
"scope": "grant"
}
]
}
}
}export_training_data 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.
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Export vault beliefs as JSONL training data for LLM finetuning. Generates instruction-following pairs from compiled beliefs: question about entity → belief body as answer. Filters out stale and low-confidence beliefs. Output is OpenAI-compatible JSONL. Uses PRISM scoring dimensions for quality-weighted sampling: only beliefs with confidence >= 0.5 and non-stale status are included in the training set. Args: output_path: Path to write JSONL file (default: training_data.jsonl) format: Output format, currently only 'jsonl' supported. It is categorised as a Write tool in the Entroly Context Engine MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
Register the Entroly Context Engine MCP server in PolicyLayer and add a rule for export_training_data: 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 Entroly Context Engine. Nothing to install.
export_training_data 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 export_training_data 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 export_training_data. 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.
export_training_data is provided by the Entroly Context Engine MCP server (juyterman1000/entroly). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from Entroly Context Engine, add the rest of your stack, and see everything your agents can call. Then put policy on all of it.
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