Predict and pre-load context that will likely be needed next. Combines static analysis (imports, callees, test files) with learned co-access patterns to predict what the agent will need. Args: file_path: The file currently being accessed source_content: The source code content (for static analysi...
AI agents call prefetch_related to retrieve information from Entroly Context Engine without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
This tool analyzes code structure and access patterns to optimize context window preparation. It retrieves and loads related files/context based on predictions, but does not execute code, modify files, delete data, or commit financial transactions.
From the tool's definition Tool performs predictive pre-loading of context through 'static analysis' and 'co-access patterns' without modifying data. The description explicitly states it 'predict[s] and pre-load[s]' (retrieve operation) rather than create, modify, delete, or execute.
Documented attack patterns abuse exactly the kind of access prefetch_related 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 prefetch_related:
{
"version": "1",
"default": "deny",
"tools": {
"prefetch_related": {}
}
}prefetch_related is read-only, so it stays allowed — but everything else on the server is denied unless you say otherwise.
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Predict and pre-load context that will likely be needed next. Combines static analysis (imports, callees, test files) with learned co-access patterns to predict what the agent will need. Args: file_path: The file currently being accessed source_content: The source code content (for static analysis) language: Programming language (python, typescript, rust). It is categorised as a Read tool in the Entroly Context Engine MCP Server, which means it retrieves data without modifying state.
Register the Entroly Context Engine MCP server in PolicyLayer and add a rule for prefetch_related: 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.
prefetch_related 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 prefetch_related 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 prefetch_related. 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.
prefetch_related 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.
Free to start. No card required.
52 Entroly Context Engine tools catalogued and risk-classified — across an index of 43,000+ MCP servers.