answer_detailed · Openai Responses MCP: Risk & Policy

WHAT IT DOES

What answer_detailed does on Openai Responses

AI agents invoke answer_detailed to trigger actions in Openai Responses. What it does depends on the arguments the agent supplies, and its effects often reach beyond the immediate call — builds kicked off, notifications sent, workflows started.

WHY IT NEEDS A POLICY

Why answer_detailed needs a policy

This tool invokes the OpenAI Responses API and performs web searches to answer questions. These are external operations whose effects depend on the query arguments. It is not purely a read (it triggers live external API calls and search operations), making Execute the most appropriate category.

From the tool's definition "Perform comprehensive analysis with thorough research" and server description mentions "built-in web search" and "OpenAI Responses API" — triggers external API calls and web search operations

Attacks that exploit this kind of access

Recommended rule Ready

Verdict

answer_detailed Rate-limited

answer_detailed stays usable, but rate-capped — a runaway agent can't fire it dozens of times a minute. Everything else on the server is denied unless you say otherwise.

View as policy code

policy.json

{
  "version": "1",
  "default": "deny",
  "tools": {
    "answer_detailed": {
      "limits": [
        {
          "counter": "answer_detailed_rate",
          "window": "minute",
          "max": 10,
          "scope": "grant"
        }
      ]
    }
  }
}

RATE-LIMIT THIS TOOL →

Instant setup, no code required.

PolicyLayer is an MCP gateway: every tool call is checked against your rule before it reaches Openai Responses, so you can run agents against it safely.

FAQ

Questions about answer_detailed

What does the answer_detailed tool do? +

Perform comprehensive analysis with thorough research and detailed explanations. Best for complex questions requiring deep investigation. It is categorised as a Execute tool in the Openai Responses MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.

How do I enforce a policy on answer_detailed? +

Register the Openai Responses MCP server in PolicyLayer and add a rule for answer_detailed: 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 Openai Responses. Nothing to install.

What risk level is answer_detailed? +

answer_detailed is a Execute tool with high risk. Execute tools should be rate-limited and have argument validation enabled.

Can I rate-limit answer_detailed? +

Yes. Add a rate_limit block to the answer_detailed 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_detailed completely? +

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

answer_detailed is provided by the Openai Responses MCP server (uchimanajet7/openai-responses-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.