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.
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What explain_recommendation does on Elementify MCP
AI agents call explain_recommendation to retrieve information from Elementify MCP without modifying anything. It is typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
Why explain_recommendation is rated Low
This tool retrieves and returns informational content (guides, descriptions, validation steps) for a given recommendation. It does not modify, execute, or delete anything — it is a read/fetch operation that surfaces documentation or instructions for the user to act on separately.
From the tool's definition Get a detailed, step-by-step guide for a specific recommendation... Returns the full problem description, root cause, exact tool calls to execute, expected output, and validation steps.
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 is read-only, so it stays allowed. 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 Read tool in the Elementify MCP MCP Server, which means it retrieves data without modifying state.
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 Read tool with low risk. Read-only tools are generally safe to allow by default.
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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