get_recommendations
Run the assessment and recommendation engine. Returns a prioritized, context-aware list of next steps for the site — filtered by user role, site purpose, and current state. Each recommendation includes the MCP tools needed to execute it and any dependencies. Use this as the entry point for any wi...
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What get_recommendations does on Elementify MCP
AI agents call get_recommendations 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 get_recommendations is rated Low
This tool generates recommendations based on site assessment but does not perform actions—it retrieves and returns a list. It serves as an informational entry point for workflows rather than executing changes. The actual modifications would be performed by other sibling tools (like addon_ecosystem_wizard, advanced_creator_mode, etc.) that the user would invoke based on these recommendations.
From the tool's definition The tool description states it 'Returns a prioritized, context-aware list of next steps' and 'recommendations include the MCP tools needed to execute it.' The verb 'Returns' and 'assessment and recommendation engine' indicate this tool queries and retrieves…
Attacks that exploit this kind of access
The rule that runs get_recommendations 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 get_recommendations, this is the rule to start with:
get_recommendations 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 get_recommendations call is checked against it from then on.
Questions about get_recommendations
Run the assessment and recommendation engine. Returns a prioritized, context-aware list of next steps for the site — filtered by user role, site purpose, and current state. Each recommendation includes the MCP tools needed to execute it and any dependencies. Use this as the entry point for any wizard workflow. 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 get_recommendations: 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.
get_recommendations 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 get_recommendations 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 get_recommendations. 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.
get_recommendations 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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