How to Use the Feathery MCP in AutoGen

Let your AutoGen agents debate and execute complex Feathery form operations together.

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Connect Feathery MCP to AutoGen

Create your Vinkius account to connect Feathery 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 Feathery with AutoGen

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Perplexity

Debate form logic in AutoGen

Assign one agent to `get_form_details` and another to verify the logic against your business requirements. They discuss the structure before finalizing any changes. This prevents bad configurations. Your agents challenge each other to ensure the form details align with your actual goals.

Audit connector logs with agents

Have a specialized security agent pull `list_connector_logs` to hunt for anomalies. It reports findings to a manager agent that decides the next step. It turns a tedious manual audit into a conversation. Your agents parse the logs, flag errors, and suggest fixes in real-time.

Consensus-driven user management

Use `list_users` and `get_user_data` to gather info for your agents. They debate the user's status based on the retrieved data and agree on the correct action. This ensures every user update is vetted. No single agent makes a move until the team agrees the data is valid.

Setup guide

Set up Feathery 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 Feathery 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="Feathery_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

Yes. Agents can call the tools and debate the results. It's perfect for complex tasks where you need multiple perspectives on your data.

Pass the tool list to your AssistantAgent constructor. The adapter handles the rest, letting your agents start calling tools immediately.

The server uses a standard endpoint token. Data is only accessible to the agents you authorize, keeping your sensitive information contained.

They can. You can task an agent with checking the connector logs, and it will flag any issues for the rest of the team.

It uses your existing API credentials. The server respects your account limits, and your agents only see what your token allows.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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