Medium Risk

send_conversation_reply

Send a user reply in an ongoing conversation (interview or table-maker). After sending, poll get_conversation for the AI's next response.

Part of the Hyperplexity server.

send_conversation_reply can modify Hyperplexity data, with no limits today. PolicyLayer puts allow, deny, and rate-limit rules on every call. Live in minutes.

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AI agents use send_conversation_reply to create or modify resources in Hyperplexity. Write operations carry medium risk because an autonomous agent could trigger bulk unintended modifications. Rate limits prevent a single agent session from making hundreds of changes in rapid succession. Argument validation ensures the agent passes expected values.

Without a policy, an AI agent could call send_conversation_reply repeatedly, creating or modifying resources faster than any human could review. PolicyLayer's rate limiting ensures write operations happen at a controlled pace, and argument validation catches malformed or unexpected inputs before they reach Hyperplexity.

Write tools can modify data. A rate limit prevents runaway bulk operations from AI agents.

policy.json
{
  "version": "1",
  "default": "deny",
  "tools": {
    "send_conversation_reply": {
      "limits": [
        {
          "counter": "send_conversation_reply_rate",
          "window": "minute",
          "max": 30,
          "scope": "grant"
        }
      ]
    }
  }
}

See the full Hyperplexity policy for all 16 tools.

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These attack patterns abuse exactly the kind of access send_conversation_reply 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 send_conversation_reply only ever does what you allow.

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Other write tools across the catalogue. The same approach applies to each: rate-limit and validate the arguments.

What does the send_conversation_reply tool do? +

Send a user reply in an ongoing conversation (interview or table-maker). After sending, poll get_conversation for the AI's next response.. It is categorised as a Write tool in the Hyperplexity MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.

How do I enforce a policy on send_conversation_reply? +

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

What risk level is send_conversation_reply? +

send_conversation_reply is a Write tool with medium risk. Write tools should be rate-limited to prevent accidental bulk modifications.

Can I rate-limit send_conversation_reply? +

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

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

send_conversation_reply is provided by the Hyperplexity MCP server (hyperplexity/hyperplexity). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.

Enforce policy on every Hyperplexity tool call.

Deterministic rules across all 16 Hyperplexity tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.

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