Low Risk

recommend_skills

Get personalized skill recommendations based on a project description. Returns a JSON array of 5 suggested skills ranked by relevance, each with slug, name, description, quality_score, stars, tags, and install_command. Use this when starting a new project to discover relevant tools, or when you n...

Part of the Loaditout server.

recommend_skills is read-only, but an agent in a loop can still rack up calls and cost. PolicyLayer caps every call before it runs. Live in minutes.

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AI agents call recommend_skills to retrieve information from Loaditout without modifying any data. This is common in research, monitoring, and reporting workflows where the agent needs context before taking action. Because read operations don't change state, they are generally safe to allow without restrictions -- but you may still want rate limits to control API costs.

Even though recommend_skills only reads data, uncontrolled read access can leak sensitive information or rack up API costs. An agent caught in a retry loop could make thousands of calls per minute. A rate limit gives you a safety net without blocking legitimate use.

Read-only tools are safe to allow by default. No rate limit needed unless you want to control costs.

policy.json
{
  "version": "1",
  "default": "deny",
  "tools": {
    "recommend_skills": {}
  }
}

See the full Loaditout policy for all 21 tools.

Get this rule live on your own Loaditout server in minutes. PolicyLayer enforces it on every call, before it runs.

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View all 21 tools →

These attack patterns abuse exactly the kind of access recommend_skills gives an agent. Each links to the full case and the policy that stops it:

Browse the full MCP Attack Database →

Every attack above starts with a tool call. PolicyLayer checks each one against your policy first, so recommend_skills only ever does what you allow.

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Other read tools across the catalogue. The same approach applies to each: allow, with a rate cap to control cost.

What does the recommend_skills tool do? +

Get personalized skill recommendations based on a project description. Returns a JSON array of 5 suggested skills ranked by relevance, each with slug, name, description, quality_score, stars, tags, and install_command. Use this when starting a new project to discover relevant tools, or when you need capabilities beyond your current toolset. Do not use this for searching by keyword (use search_skills instead). Requires a descriptive context string for accurate recommendations.. It is categorised as a Read tool in the Loaditout MCP Server, which means it retrieves data without modifying state.

How do I enforce a policy on recommend_skills? +

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

What risk level is recommend_skills? +

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

Can I rate-limit recommend_skills? +

Yes. Add a rate_limit block to the recommend_skills 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_skills completely? +

Set action: deny in the PolicyLayer policy for recommend_skills. 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_skills? +

recommend_skills is provided by the Loaditout MCP server (loaditout-mcp-server). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.

Enforce policy on every Loaditout tool call.

Deterministic rules across all 21 Loaditout tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.

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