explain_recommendation
Get a detailed, step-by-step guide for a specific recommendation from get_recommendations. Returns the full problem description, root cause, exact tool calls to execute, expected output, and validation steps. Use this as the entry point for guided wizard flows.
This record as markdown: /tools/vamerli-elementify-mcp/explain-recommendation.md
What explain_recommendation does on Elementify MCP
AI agents invoke explain_recommendation to trigger actions in Elementify MCP. What it does depends on the arguments the agent supplies, and its effects often reach beyond the immediate call: builds kicked off, notifications sent, workflows started.
Why explain_recommendation is rated High
explain_recommendation triggers real processes with real consequences. An agent gone sideways doesn't fire it once. It starts dozens of builds, sends mass notifications, or burns through compute before anyone looks up.
Risk signalsAdmin/system-level operation
Attacks that exploit this kind of access
The rule that runs explain_recommendation safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Elementify MCP, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For explain_recommendation, this is the rule to start with:
explain_recommendation stays usable, but rate-capped: a runaway agent can't fire it dozens of times a minute. Everything else on the server is denied unless you say otherwise.
The button opens the PolicyLayer dashboard: create your workspace, connect Elementify MCP, apply this rule, and every explain_recommendation call is checked against it from then on.
Questions about explain_recommendation
Get a detailed, step-by-step guide for a specific recommendation from get_recommendations. Returns the full problem description, root cause, exact tool calls to execute, expected output, and validation steps. Use this as the entry point for guided wizard flows. It is categorised as a Execute tool in the Elementify MCP MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the Elementify MCP server in PolicyLayer and add a rule for explain_recommendation: 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 Elementify MCP. Nothing to install.
explain_recommendation is a Execute tool with high risk. Execute tools should be rate-limited and have argument validation enabled.
Yes. Add a rate_limit block to the explain_recommendation 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_recommendation. 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_recommendation is provided by the Elementify MCP server (vamerli/elementify-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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