How to Use the Yida MCP in AutoGen

Achieve consensus decisions using Yida MCP Server with AutoGen.

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Connect Yida MCP to AutoGen

Create your Vinkius account to connect Yida 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 Yida with AutoGen

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Perplexity

Negotiating Record Updates via AutoGen

The agents can debate the best way to update data. A 'validation agent' calls `get_form_schema` first, while an 'update agent' uses `update_record`. They challenge each other until a consensus on the necessary changes is reached.

Automated Workflow Approval Debates with AutoGen

You can simulate complex business rules. Agents discuss whether a workflow needs modification by calling `get_instance_timeline` and comparing it to available forms using `list_forms`. The final decision is based on the group's deliberation.

System Audit through Multi-Agent MCP Server

Build a governance system where agents cross-check data. One agent lists all users (`list_users`), and another uses `get_record_details` to verify specific records against those user accounts, forcing a peer review process.

Setup guide

Set up Yida 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 Yida 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="Yida_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

Get your connection token →

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Common questions about Yida MCP in AutoGen

The agents don't just execute; they talk through it. They debate conclusions based on the live data provided by the MCP Server, ensuring a consensus-driven outcome.

Yida provides the ground truth for the discussion. Whether it's listing all available workflows (`list_workflows`) or checking user permissions, the MCP Server feeds objective data to the debating agents.

Yes. You can set up a deliberation process where multiple agents must agree before the `delete_record` tool is called, adding an extra layer of safety to data removal.

AutoGen's architecture supports parallel discussions. The MCP Server manages the concurrent calls needed for agents to gather differing perspectives simultaneously.

The server provides core application entities: user identities (`list_users`), form definitions, and record instances. This scope guides the agents' conversations.

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