How to Use the Bot9 MCP in OpenAI Agents SDK

Q: Can the OpenAI Agents SDK trigger a Bot9 message?

Yes. Simply invoke the sendmessage tool within your agent's execution loop. The server will relay your request to the bot and return the response immediately.

Q: Is it hard to list bots using the OpenAI Agents SDK?

Not at all. Just call the listbots tool through the MCP interface and iterate over the returned list in your Python code.

Build production agents with the Bot9 MCP Server and OpenAI Agents SDK to keep your customer support logic under control.

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Connect Bot9 MCP to OpenAI Agents SDK

Create your Vinkius account to connect Bot9 to OpenAI Agents SDK and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Bot9 with OpenAI Agents SDK

Ask AI about this MCP

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Train bots with source URLs

Feed your support bot raw knowledge by pointing `add_data_source` at your documentation URLs. The OpenAI Agents SDK picks these up automatically, letting your agent ingest context without manual intervention. Once the source is live, check your current knowledge base using `list_data_sources`. It keeps your agent grounded in your actual product facts rather than generic training data.

Full conversation oversight

Monitor what your agents say by pulling logs with `get_conversation_history`. The OpenAI Agents SDK handles the connection, allowing you to audit bot behavior in your production dashboard. Need to see who is currently active? Use `list_conversations` to filter through ongoing sessions and identify where your support flow might need human intervention.

Manage your bot fleet

Create and track your agents using `create_bot` and `list_bots`. By connecting this MCP Server to your codebase, you can spin up specialized bots for different support tiers directly from your Python logic. Use `get_bot` to pull specific configuration details for any active instance. This ensures your code stays synced with the live support environment.

Setup guide

Set up Bot9 MCP in OpenAI Agents SDK

Prerequisites

Python 3.10+ installed
openai-agents package (pip install openai-agents)
Active Vinkius subscription with a valid endpoint token

1

Install the SDK

Run pip install openai-agents to install the OpenAI Agents SDK. The MCP integration is built-in — no extra dependencies needed.
2

Connect via SSE transport

Use MCPServerSse with your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. The SDK auto-discovers all Bot9 tools at runtime.
3

Create your Agent

Pass the MCP to Agent(mcp_servers=[server]). The agent receives Bot9 tools as native definitions — JSON schemas resolve automatically.
4

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate Bot9 tools and returns structured results. Copy the full example on the right to get started.

agent.py

import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerSse

async def main():
    async with MCPServerSse(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ) as server:
        agent = Agent(
            name="Bot9 Agent",
            instructions="You have access to Bot9 tools.",
            mcp_servers=[server],
        )
        result = await Runner.run(agent, "List recent transactions")
        print(result.final_output)

asyncio.run(main())

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Common questions about Bot9 MCP in OpenAI Agents SDK

You instantiate an MCPServerStreamableHttp object with your endpoint. Pass that into your Agent constructor to let the framework auto-discover every available tool.

Yes. Simply invoke the `send_message` tool within your agent's execution loop. The server will relay your request to the bot and return the response immediately.

Absolutely. Since the framework validates tool inputs, you can wrap Bot9 calls in your custom safety checks to prevent unauthorized configuration changes.

Not at all. Just call the `list_bots` tool through the MCP interface and iterate over the returned list in your Python code.

Your conversation logs are encrypted in transit. We only handle the raw text strings and metadata required to fulfill the agent's support queries.

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