How to Use the Zuplo MCP in AutoGen

Q: Can I check multiple Zuplo environments with AutoGen?

Yes. The agents use listenvironments to gather all available context (like 'working' and 'production'). They then debate which environment is the correct target for a sensitive operation.

Q: Does Zuplo help track configuration data for AutoGen?

Yes. Agents can validate configurations by using listfiles. This allows them to argue over the correct settings and ensure that the final, agreed-upon state matches the documented project files.

Facilitate consensus on Zuplo API gateway decisions with AutoGen.

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Connect Zuplo MCP to AutoGen

Create your Vinkius account to connect Zuplo 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 Zuplo with AutoGen

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Perplexity

Debate best practices for project management.

In a multi-agent setup, agents can debate the necessity of new infrastructure. One agent might suggest adding a domain via `list_custom_domains`, while another checks if it already exists. The resulting deliberation converges on whether creating a resource is necessary or if an existing one suffices.

Review and challenge current API consumer access rights.

Security agents can run `list_consumers` to review who has access. If risk is flagged, the consensus process leads to calling `delete_consumer`, ensuring no unauthorized keys remain. The debate centers on whether the removal of the key improves security more than current operational needs.

Determine optimal deployment status and metrics.

Agents can argue over system health. One agent might run `get_metrics` to check latency, while a performance agent suggests comparing those numbers against historical data pulled using `list_deployments`. The final consensus dictates the required action: update or leave the deployment alone.

Setup guide

Set up Zuplo 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 Zuplo 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="Zuplo_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

Get your connection token →

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Common questions about Zuplo MCP in AutoGen

AutoGen enables agents to debate complex API management decisions. For example, they can discuss whether a new project needs its own dedicated consumer key or if it should reuse an existing one.

Yes. The agents use `list_environments` to gather all available context (like 'working' and 'production'). They then debate which environment is the correct target for a sensitive operation.

The MCP Server touches details about API key consumers. Agents use `list_consumers` to gather facts, and then debate if certain consumer keys need deletion or modification based on policy.

AutoGen agents manage the lifecycle by debating deployment changes. They use `list_deployments` to review current states and may ultimately decide to call `create_deployment` if a consensus is reached.

Yes. Agents can validate configurations by using `list_files`. This allows them to argue over the correct settings and ensure that the final, agreed-upon state matches the documented project files.

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