How to Use the Dagger (Programmable CI) MCP in AutoGen

Let your AutoGen agents debate build strategies using the Dagger (Programmable CI) MCP Server.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect Dagger (Programmable CI) MCP to AutoGen

Create your Vinkius account to connect Dagger (Programmable CI) 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.

GDPR Free for Subscribers

Setup Dagger (Programmable CI) with AutoGen

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Debate builds with AutoGen

Give your agents access to `execute_graphql_query` so they can propose and critique build pipelines. One agent suggests a DAG, while another verifies the logic against the Dagger engine. This forces your agents to reach a consensus on the best build path. They negotiate the steps until they agree on the final execution order.

Secure secrets with AutoGen agents

Assign a security agent to handle `query_secret` calls. It reviews the requested secret source before the execution agent runs the build command. You prevent unauthorized access by requiring a two-agent handshake. The security agent checks the env or path format, ensuring only safe parameters reach the engine.

Manage build versions in AutoGen

Use `query_version` to keep your agents updated on the Dagger engine capabilities. A performance agent checks the version to decide which GraphQL features to use. This keeps your agents in sync with your infrastructure. They adapt their build strategies based on the specific version of the engine currently running.

Setup guide

Set up Dagger (Programmable CI) 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 Dagger (Programmable CI) 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="Dagger (Programmable CI)_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

Get your connection token →

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="Dagger (Programmable CI)_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Dagger (Programmable CI) 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 Dagger. 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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect Dagger (Programmable CI) now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about Dagger (Programmable CI) MCP in AutoGen

Agents communicate the output of tool calls within their chat history. The conversation log tracks which agent invoked which tool and how the engine responded.

Yes, you provide the tool list to the AssistantAgent constructor. Different agents use their individual prompts to decide when to trigger a build operation.

You install the extension and pass the server parameters to the tool adapter. The adapter handles the schema conversion so your agents can call tools immediately.

It supports both stdio and HTTP. You pick the transport that fits your deployment, and the agents handle the tool calls identically in either case.

All tool-specific credentials are encrypted in transit between the agent and the server. We keep the execution context ephemeral, clearing the data as soon as the agents finish their deliberation.

Use it with your favorite AI tools

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

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript