How to Use the Tray.io MCP in AutoGen

Q: Can AutoGen debate which solutions are available in Tray.io?

Yes. The agents use listavailableconnectors to gather facts and listintegrationsolutions to argue for the best setup method. It's a structured, multi-step debate about technical feasibility.

Build consensus-driven systems with AutoGen: Multi-agent deliberation over Tray.io's automation data.

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Connect Tray.io MCP to AutoGen

Create your Vinkius account to connect Tray.io 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 Tray.io with AutoGen

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Perplexity

Debating the best workflow path.

Need to figure out which automation should run? Your agents can debate this using tools like `list_workflows`. One agent might check all available workflows, while another cross-references user details using `get_authenticated_user` to argue for the optimal sequence. This consensus process ensures the final decision isn't just based on a single tool call but on multiple perspectives.

Analyzing connection readiness.

If an agent is unsure how two services connect, they can use `list_available_connectors` and `list_integration_solutions`. The agents debate which integration template makes the most sense for the user's current goal. It simulates a consultation: Agent A proposes using Slack, while Agent B points out that Salesforce has better data coverage for this task.

Debugging failures through deliberation.

When an automation fails, agents can use `list_workflow_executions` to pull the run history. They then debate the cause: Was it a permission issue (checked via `get_authenticated_user`) or was the workflow configuration wrong? The discussion leads to fixing the root problem. This multi-agent system makes debugging robust and conversational.

Setup guide

Set up Tray.io 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 Tray.io 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="Tray.io_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Tray.io 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="Tray.io_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Tray.io 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 Tray.io. 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 Tray.io MCP in AutoGen

The MCP Server provides multiple viewpoints (tools) to the agents. For example, Agent A can check user permissions (`get_authenticated_user`), and Agent B can check workflow status (`list_workflows`). They then negotiate a final action.

Yes. The agents use `list_available_connectors` to gather facts and `list_integration_solutions` to argue for the best setup method. It's a structured, multi-step debate about technical feasibility.

Use it when simple status checks aren't enough. If you need to know *why* an automation failed or what the best fix is, having agents debate the `list_workflow_executions` data provides a superior conclusion.

It helps by allowing multiple agents to validate requirements. One agent checks user permissions, while another verifies the existence of necessary connectors using `list_available_connectors`.

This server touches user identity and account configuration data, specifically retrieving details for the currently authenticated user via `get_authenticated_user`.

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