Beekeeper MCP Server for OpenAI Agents SDK 10 tools — connect in under 2 minutes
The OpenAI Agents SDK enables production-grade agent workflows in Python. Connect Beekeeper through Vinkius and your agents gain typed, auto-discovered tools with built-in guardrails. no manual schema definitions required.
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Vinkius supports streamable HTTP and SSE.
import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerStreamableHttp
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
async with MCPServerStreamableHttp(
url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
) as mcp_server:
agent = Agent(
name="Beekeeper Assistant",
instructions=(
"You help users interact with Beekeeper. "
"You have access to 10 tools."
),
mcp_servers=[mcp_server],
)
result = await Runner.run(
agent, "List all available tools from Beekeeper"
)
print(result.final_output)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About Beekeeper MCP Server
Connect your Beekeeper account to any AI agent and streamline your internal communications and frontline management through natural conversation.
The OpenAI Agents SDK auto-discovers all 10 tools from Beekeeper through native MCP integration. Build agents with built-in guardrails, tracing, and handoff patterns. chain multiple agents where one queries Beekeeper, another analyzes results, and a third generates reports, all orchestrated through Vinkius.
What you can do
- User & Group Management — List all employees and groups to maintain an organized organizational structure.
- Stream & Post Control — Manage communication channels (streams) and publish updates to keep everyone informed.
- Direct Messaging — Send messages and retrieve conversation histories to facilitate instant communication.
- Tenant Insights — Access tenant information and system metadata for administrative oversight.
- Advanced Search — Quickly find specific users by name or email to coordinate efforts effectively.
The Beekeeper MCP Server exposes 10 tools through the Vinkius. Connect it to OpenAI Agents SDK in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect Beekeeper to OpenAI Agents SDK via MCP
Follow these steps to integrate the Beekeeper MCP Server with OpenAI Agents SDK.
Install the SDK
Run pip install openai-agents in your Python environment
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com
Run the script
Save the code above and run it: python agent.py
Explore tools
The agent will automatically discover 10 tools from Beekeeper
Why Use OpenAI Agents SDK with the Beekeeper MCP Server
OpenAI Agents SDK provides unique advantages when paired with Beekeeper through the Model Context Protocol.
Native MCP integration via `MCPServerSse`, pass the URL and the SDK auto-discovers all tools with full type safety
Built-in guardrails, tracing, and handoff patterns let you build production-grade agents without reinventing safety infrastructure
Lightweight and composable: chain multiple agents and MCP servers in a single pipeline with minimal boilerplate
First-party OpenAI support ensures optimal compatibility with GPT models for tool calling and structured output
Beekeeper + OpenAI Agents SDK Use Cases
Practical scenarios where OpenAI Agents SDK combined with the Beekeeper MCP Server delivers measurable value.
Automated workflows: build agents that query Beekeeper, process the data, and trigger follow-up actions autonomously
Multi-agent orchestration: create specialist agents. one queries Beekeeper, another analyzes results, a third generates reports
Data enrichment pipelines: stream data through Beekeeper tools and transform it with OpenAI models in a single async loop
Customer support bots: agents query Beekeeper to resolve tickets, look up records, and update statuses without human intervention
Beekeeper MCP Tools for OpenAI Agents SDK (10)
These 10 tools become available when you connect Beekeeper to OpenAI Agents SDK via MCP:
create_post
Create a new post in a stream
get_tenant_info
Retrieve Beekeeper tenant information
get_user
Get details of a specific user
list_groups
List Beekeeper groups
list_messages
List messages in a conversation
list_posts
List posts in a specific stream
list_streams
List Beekeeper streams (channels)
list_users
List all Beekeeper users
search_users
Search for users by name or email
send_message
Send a direct message to a user
Example Prompts for Beekeeper in OpenAI Agents SDK
Ready-to-use prompts you can give your OpenAI Agents SDK agent to start working with Beekeeper immediately.
"List all active communication streams on Beekeeper."
"Post to stream str_2: 'Reminder: New safety protocols start tomorrow morning.'"
"Find the user ID for 'Sarah Miller'."
Troubleshooting Beekeeper MCP Server with OpenAI Agents SDK
Common issues when connecting Beekeeper to OpenAI Agents SDK through the Vinkius, and how to resolve them.
MCPServerStreamableHttp not found
pip install --upgrade openai-agentsAgent not calling tools
Beekeeper + OpenAI Agents SDK FAQ
Common questions about integrating Beekeeper MCP Server with OpenAI Agents SDK.
How does the OpenAI Agents SDK connect to MCP?
MCPServerSse(url=...) to create a server connection. The SDK auto-discovers all tools and makes them available to your agent with full type information.Can I use multiple MCP servers in one agent?
MCPServerSse instances to the agent constructor. The agent can use tools from all connected servers within a single run.Does the SDK support streaming responses?
Connect Beekeeper with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
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GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Beekeeper to OpenAI Agents SDK
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.