Low Risk

earning_opportunities

List the ways an agent can put its assets to work through this MCP — a static "how to earn" menu (no balances read, no network calls). Surface this to an agent right after onboarding (e.g. once identity_register succeeds) so it knows the menu of strategies available: which asset each needs, the k...

Part of the Aibtc server.

earning_opportunities 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 earning_opportunities to retrieve information from Aibtc 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 earning_opportunities 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": {
    "earning_opportunities": {}
  }
}

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

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Every attack above starts with a tool call. PolicyLayer checks each one against your policy first, so earning_opportunities 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 earning_opportunities tool do? +

List the ways an agent can put its assets to work through this MCP — a static "how to earn" menu (no balances read, no network calls). Surface this to an agent right after onboarding (e.g. once identity_register succeeds) so it knows the menu of strategies available: which asset each needs, the kind of return, and the tool that starts it. Each entry: { id, name, assets, returns, summary, start_with, reference? } where start_with lists the MCP tools to begin, in order. Optional asset filter (e.g. "STX", "sBTC") returns only strategies that put that asset to work — useful for "what can I do with the STX I hold?". Matching is case-insensitive substring over each strategy's assets. Aligned with aibtc.com. The trading competition and news correspondent are intentionally not part of this menu.. It is categorised as a Read tool in the Aibtc MCP Server, which means it retrieves data without modifying state.

How do I enforce a policy on earning_opportunities? +

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

What risk level is earning_opportunities? +

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

Can I rate-limit earning_opportunities? +

Yes. Add a rate_limit block to the earning_opportunities 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 earning_opportunities completely? +

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

earning_opportunities is provided by the Aibtc MCP server (@aibtc/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 Aibtc tool call.

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