How to Use the AirMatch Pro MCP in AutoGen

Q: How can two AutoGen agents collaborate on a single AirMatch Pro proposal?

Assign one agent to draft it with createproposal. Then, have a second agent getproposal using the returned ID to review and suggest edits. They can converse until they agree on a final version.

Q: Can I use AutoGen to create a 'watchdog' agent for my AirMatch Pro account?

Yes. Create an agent that periodically runs listjobs and listproposals. It can then flag any jobs that have been open too long or proposals that are stalled, and report back to you.

Let your agents debate the best hiring strategy. Use AutoGen's multi-agent conversations to make smarter decisions with AirMatch Pro.

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Connect AirMatch Pro MCP to AutoGen

Create your Vinkius account to connect AirMatch Pro 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 AirMatch Pro with AutoGen

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Set Up a Digital Hiring Committee

Create a team of specialized agents. One agent, the "Sourcer," uses `list_leads` to find candidates. A second agent, the "Reviewer," uses `get_lead` to analyze their details and critique the Sourcer's choices. This isn't a simple script; it's a conversation. The agents debate the pros and cons of each candidate, arriving at a consensus on who is best suited to receive an offer via `create_proposal`.

Negotiate Proposals with Competing Agents

You can model complex business logic. Have a "Finance" agent that scrutinizes every new proposal from `create_proposal` for profitability, while a "Sales" agent argues for closing the deal quickly. AutoGen lets them discuss and revise the plan. The Finance agent might push back on a low price, forcing the Sales agent to adjust the proposal before it gets finalized. It's a real negotiation, handled by your code.

Your AutoGen Agents Use the MCP Server

Connecting to AirMatch Pro is straightforward. The `autogen-ext[mcp]` package provides a function that pulls all available tools from the MCP server. Just pass this tool list to your `AssistantAgent`. The adapter handles converting the tool schemas automatically, so your agents can start using tools like `list_customers` and `list_jobs` in their conversations right away.

Setup guide

Set up AirMatch Pro 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 AirMatch Pro 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="AirMatch Pro_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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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="AirMatch Pro_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

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

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by AirMatch Pro. 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 AirMatch Pro MCP in AutoGen

Assign one agent to draft it with `create_proposal`. Then, have a second agent `get_proposal` using the returned ID to review and suggest edits. They can converse until they agree on a final version.

Yes. Create an agent that periodically runs `list_jobs` and `list_proposals`. It can then flag any jobs that have been open too long or proposals that are stalled, and report back to you.

AutoGen's conversational output shows you which agent called which tool (`get_lead`, `create_proposal`, etc.) and with what arguments. This lets you follow the logic and see where a decision went wrong.

You get consensus-driven actions. Instead of one agent making a potentially flawed decision, multiple agents challenge each other, leading to more robust outcomes when using your AirMatch Pro tools.

The agent conversations, including any AirMatch Pro data like lead or customer details they discuss, happen within your AutoGen environment. The Vinkius MCP Server only executes the specific tool calls and has no access to the surrounding debate.

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