Explain why files were selected into a context bundle using the shared retrieval provenance and explainability contract. This tool reuses the same selection vocabulary exposed by graph-aware retrieval and get_context_for_prompt instead of inventing a parallel explanation path.
AI agents call why_this_context to retrieve information from Context Engine MCP Server without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
This is a query/explanation tool that provides transparency into semantic search selections. It retrieves and presents provenance information about context bundling decisions—a read-only analytical operation with no side effects or destructive capacity. The low severity reflects minimal blast radius: misuse would at worst generate confusing explanations, not damage data or trigger unintended external actions.
From the tool's definition The tool 'explain[s] why files were selected' and 'reuses...retrieval...get_context_for_prompt' — operations that query and explain existing selections without modifying state.
Documented attack patterns abuse exactly the kind of access why_this_context gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and Context Engine MCP Server, and nothing reaches the server without passing your rules. This is the rule we recommend for why_this_context:
{
"version": "1",
"default": "deny",
"tools": {
"why_this_context": {}
}
}why_this_context is read-only, so it stays allowed — but everything else on the server is denied unless you say otherwise.
Free to start. No card required.
Explain why files were selected into a context bundle using the shared retrieval provenance and explainability contract. This tool reuses the same selection vocabulary exposed by graph-aware retrieval and get_context_for_prompt instead of inventing a parallel explanation path. It is categorised as a Read tool in the Context Engine MCP Server MCP Server, which means it retrieves data without modifying state.
Register the Context Engine MCP Server MCP server in PolicyLayer and add a rule for why_this_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 Context Engine MCP Server. Nothing to install.
why_this_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 why_this_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 why_this_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.
why_this_context is provided by the Context Engine MCP Server MCP server (kirachon/context-engine). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from Context Engine MCP Server, 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.
50 Context Engine MCP Server tools catalogued and risk-classified — across an index of 43,000+ MCP servers.