How to Use the Ada MCP in AutoGen

Q: How do I connect Ada to AutoGen?

Run pip install -U autogen-extmcp. Use mcpservertools with StreamableHttpServerParams pointing to your Vinkius URL, then pass the output to your assistant.

Q: Can AutoGen agents read my Ada articles?

They call listarticles to retrieve the catalog. A research agent parses the array and shares the findings with the rest of the group chat.

Let AutoGen agents debate customer support strategies using live Ada conversation data.

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Connect Ada MCP to AutoGen

Create your Vinkius account to connect Ada 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 Ada with AutoGen

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Perplexity

Multi-agent knowledge base updates

The `create_article` tool executes only after your AutoGen agents reach a consensus on the documentation gap. A technical writer agent drafts the content while a compliance agent reviews it for accuracy before pushing it to Ada. They negotiate the exact phrasing of the title and text content. You build a system where the answer requires deliberation, ensuring no single agent blindly publishes bad help docs.

Agents analyzing support chats

Fetching data with `list_conversations` feeds raw chat transcripts into a group chat environment. Multiple agents read the same Ada bot interaction and challenge each other's conclusions about why the customer got frustrated. A sentiment agent flags negative language while a product agent looks for UX bugs. They discuss the findings until they converge on a single root cause report.

AutoGen MCP Server integration

The `get_end_user` tool pulls custom metavariables into the discussion so agents know who they are analyzing. One agent requests the End User ID, and the McpToolAdapter handles the schema conversion automatically. You pass the tool list directly to the AssistantAgent constructor. The agents use Streamable HTTP transports to fetch the profile data without breaking their conversational loop.

Setup guide

Set up Ada 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 Ada 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="Ada_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

Get your connection token →

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Common questions about Ada MCP in AutoGen

Run pip install -U autogen-ext[mcp]. Use mcp_server_tools with StreamableHttpServerParams pointing to your Vinkius URL, then pass the output to your assistant.

They call list_articles to retrieve the catalog. A research agent parses the array and shares the findings with the rest of the group chat.

No manual configuration is required. The framework maps the JSON schema into native function definitions automatically so the agents know exactly what parameters to send.

You configure a speaker selection method in your group chat. The agents debate the proposed text until the designated manager agent decides to execute the write operation.

Custom metavariables and End User IDs remain isolated within a zero-trust MCP sandbox. Vinkius manages the auth layer so your agents only need a single token to access the environment.

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