How to Use the Beeline MCP in AutoGen

Q: How do I register Beeline tools in AutoGen?

Use the mcpservertools helper from autogen-extmcp pointing to your Vinkius URL. Pass the generated tools list directly into your AssistantAgent constructor.

Build AutoGen multi-agent systems that use our MCP Server to debate, audit, and approve Beeline timesheets and requisitions.

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Connect Beeline MCP to AutoGen

Create your Vinkius account to connect Beeline to AutoGen 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 Beeline with AutoGen

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Consensus-driven Beeline MCP Server workflows in AutoGen

The Beeline MCP Server exposes tools like `get_timesheet` and `list_expenses` to AutoGen's multi-agent conversation loops. You set up specialized agents—like an auditor agent and a budget agent—that discuss and verify contractor billing records. The auditor agent uses the tools to pull the raw hours, while the budget agent checks against project limits. They negotiate in a chat thread, resolving discrepancies before final approval without human intervention.

Multi-agent requisition drafting and validation

The `list_requisitions` tool fetches current openings, while `get_requisition` details the specific requirements of a role. In AutoGen, a hiring manager agent and an HR compliance agent use these tools to review open positions. The compliance agent reviews the requisition text for policy issues, while the hiring manager agent updates parameters. They iterate on the draft inside the conversation context until both agree on the final version.

Automated vendor performance reviews

The `list_suppliers` tool retrieves your active vendor list, while `list_assignments` matches contractors to their respective suppliers. Your AutoGen agents use these endpoints to compile supplier scorecard reports. A supplier relations agent analyzes the assignment duration and cost data, while a quality agent evaluates performance metrics. Together, they draft vendor performance reviews based on actual operational data.

Setup guide

Set up Beeline MCP in AutoGen

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] package
Active Vinkius subscription with a valid endpoint token

1

Install AutoGen with MCP
Run pip install "autogen-ext[mcp]" autogen-agentchat. The MCP extension includes mcp_server_tools for stateless tool access.
2

Fetch tools from the MCP
Call mcp_server_tools(SseServerParams(url=...)) with your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Run your agent
Pass the tools to AssistantAgent and call agent.run(). The agent invokes Beeline tools and returns structured results.

agent.py

from autogen_ext.tools.mcp import SseServerParams, mcp_server_tools
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

tools = await mcp_server_tools(server_params)

agent = AssistantAgent(
    name="Beeline_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Beeline data")
print(result.messages[-1].content)

Get your connection token →

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] + autogen-agentchat
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Same packages as above. McpWorkbench is ideal when your agent needs stateful sessions across multiple tool calls.
2

Use McpWorkbench as context manager
Wrap your agent in async with McpWorkbench(...) to maintain shared state and resources. The workbench manages the full MCP session lifecycle.
3

Run with workbench
Pass workbench=workbench to your agent. State is preserved across multiple tool calls within the same session.

agent.py

from autogen_ext.tools.mcp import McpWorkbench, SseServerParams
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

async with McpWorkbench(server_params) as workbench:
    agent = AssistantAgent(
        name="Beeline_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Beeline data")
    print(result.messages[-1].content)

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Beeline. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Beeline MCP in AutoGen

Use the mcp_server_tools helper from autogen-ext[mcp] pointing to your Vinkius URL. Pass the generated tools list directly into your AssistantAgent constructor.

Yes, the hosted server handles concurrent requests. Your auditor agent can check get_timesheet while your sourcing agent calls search_requisitions in the same conversation.

The adapter automatically translates the JSON schemas of Beeline MCP tools into the format required by AutoGen agents, ensuring seamless execution.

Yes, you can configure the connection using StreamableHttpServerParams. This provides real-time tool execution feedback during complex agent debates.

Your contingent workforce data, including rates, assignments, and supplier identities, is protected by Vinkius's ephemeral sandbox. No data is stored on our servers, and all API calls use secure, single-token authentication.

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