Low Risk

answer_question

Answer small-business marketing questions using Enji’s public resources; synthesizes a concise, grounded response with sources. If the underlying search result is weak (meta.is_low_confidence = true from search_public), the service returns a fallback pointing to Enji’s blog/Q&A/help/tools rather ...

Part of the Ai Marketing Agent server.

answer_question 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 answer_question to retrieve information from Ai Marketing Agent 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 answer_question 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": {
    "answer_question": {}
  }
}

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These attack patterns abuse exactly the kind of access answer_question 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 answer_question 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 answer_question tool do? +

Answer small-business marketing questions using Enji’s public resources; synthesizes a concise, grounded response with sources. If the underlying search result is weak (meta.is_low_confidence = true from search_public), the service returns a fallback pointing to Enji’s blog/Q&A/help/tools rather than inventing an answer (often when confidence <0.45).. It is categorised as a Read tool in the Ai Marketing Agent MCP Server, which means it retrieves data without modifying state.

How do I enforce a policy on answer_question? +

Register the Ai Marketing Agent MCP server in PolicyLayer and add a rule for answer_question: 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 Ai Marketing Agent. Nothing to install.

What risk level is answer_question? +

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

Can I rate-limit answer_question? +

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

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

answer_question is provided by the Ai Marketing Agent MCP server (enji/ai-marketing-agent). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.

Enforce policy on every Ai Marketing Agent tool call.

Deterministic rules across all 9 Ai Marketing Agent tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.

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