Explain why each fragment was included or excluded in the last optimization. Shows per-fragment scoring breakdowns with all dimensions visible: recency, frequency, semantic, entropy, feedback multiplier, dependency boost, criticality, and composite score. Also shows context sufficiency (what % of...
AI agents call explain_context 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 queries and displays internal analysis metadata (scoring dimensions, context sufficiency metrics, exploration swaps) without side effects. It is a post-hoc explanatory tool that reads optimization results. The passive verbs 'explain,' 'show,' and 'understand' confirm this is introspective reporting, not action-taking.
From the tool's definition Tool description states it 'Explain[s]' and 'Shows per-fragment scoring breakdowns' - purely informational retrieval of analysis results from the last optimization run. No data modification, deletion, execution, or financial operations occur.
Documented attack patterns abuse exactly the kind of access explain_context 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 explain_context:
{
"version": "1",
"default": "deny",
"tools": {
"explain_context": {}
}
}explain_context is read-only, so it stays allowed — but everything else on the server is denied unless you say otherwise.
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Explain why each fragment was included or excluded in the last optimization. Shows per-fragment scoring breakdowns with all dimensions visible: recency, frequency, semantic, entropy, feedback multiplier, dependency boost, criticality, and composite score. Also shows context sufficiency (what % of referenced symbols have definitions included) and any exploration swaps. Call this after optimize_context to understand selection decisions. 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 explain_context: 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.
explain_context 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 explain_context 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 explain_context. 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.
explain_context 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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52 Entroly Context Engine tools catalogued and risk-classified — across an index of 43,000+ MCP servers.