recommend_components

Intent-oriented component recommendation. Useful when the user describes a task or UX goal rather than knowing exact component names.

Server Musea @vizejs/musea-mcp-server
Category Read
Risk class Low
Parameters 00 required

What recommend_components does on Musea

AI agents call recommend_components to retrieve information from Musea without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.

Why recommend_components needs a policy

This is a lookup/suggestion tool that queries a design system database to recommend components matching a user's described UX goal. It has no side effects—it does not create, modify, delete, or execute code. The output is informational guidance to help users select appropriate components from the existing catalog. Misuse would result in irrelevant recommendations at worst, posing minimal security risk.

From the tool's definition Tool is described as providing 'component recommendation' based on user intent/goals. It retrieves and suggests existing components from the design system without modifying, executing, or deleting anything.

Questions about recommend_components

What does the recommend_components tool do? +

Intent-oriented component recommendation. Useful when the user describes a task or UX goal rather than knowing exact component names. It is categorised as a Read tool in the Musea MCP Server, which means it retrieves data without modifying state.

How do I enforce a policy on recommend_components? +

Register the Musea MCP server in PolicyLayer and add a rule for recommend_components: 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 Musea. Nothing to install.

What risk level is recommend_components? +

recommend_components is a Read tool with low risk. Read-only tools are generally safe to allow by default.

Can I rate-limit recommend_components? +

Yes. Add a rate_limit block to the recommend_components 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.

How do I block recommend_components completely? +

Set action: deny in the PolicyLayer policy for recommend_components. 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.

What MCP server provides recommend_components? +

recommend_components is provided by the Musea MCP server (@vizejs/musea-mcp-server). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.

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