How to Use the Aha! MCP in AutoGen

Q: How do I connect AutoGen to the Aha! MCP Server?

Install autogen-extmcp. Initialize mcpservertools with your Vinkius Streamable HTTP URL. Pass the resulting tool list to your AssistantAgent constructor.

Q: How do multiple AutoGen agents handle Aha! feature lookups?

One agent usually calls getfeature and shares the JSON response in the group chat. The other agents read that shared context and begin their deliberation based on the exact specifications.

Q: Does the MCP integration handle schema conversion automatically?

Yes. The McpToolAdapter translates the Aha! API parameters into a format the AutoGen agents understand. You skip writing custom wrappers for every endpoint.

Let AutoGen agents debate and manage your Aha! product strategy.

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Create your Vinkius account to connect Aha! 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 Aha! with AutoGen

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Aha! MCP Server for multi-agent triage

The `list_ideas` tool feeds raw product concepts from the MCP server to a panel of AutoGen agents. A customer success agent advocates for user requests while an engineering agent argues about technical debt. They debate the merits of each idea based on the actual text pulled from your backlog. Once the agents reach a consensus, the system uses `create_idea` to formalize the approved concept. You watch them negotiate priority levels before anything gets committed to the roadmap. The deliberation replaces hours of manual sprint planning meetings.

Audit feature requirements through debate

Calling `get_feature` allows your security and performance agents to inspect specific product requirements in Aha!. The security agent scans the acceptance criteria for vulnerabilities. Simultaneously, the performance agent checks for potential latency issues. If either agent finds a flaw, they challenge the current spec and propose amendments. They pull broader context using `list_features` to see if similar items share the same flaw. You get a fully vetted feature definition before a developer writes a single line of code.

Negotiate release timelines

Executing `list_releases` gives your AutoGen system the hard deadlines for upcoming launches. A project management agent reviews these dates and cross-references them against the active workload. If the schedule looks impossible, it alerts the rest of the agent swarm. The group then discusses which items to cut to meet the deadline. They review the active backlog and negotiate a revised scope based on the API data. You receive a realistic, debated release plan instead of a blind guess.

Setup guide

Set up Aha! 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 Aha! 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="Aha!_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Aha!. 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 Aha! MCP in AutoGen

Install `autogen-ext[mcp]`. Initialize `mcp_server_tools` with your Vinkius Streamable HTTP URL. Pass the resulting tool list to your `AssistantAgent` constructor.

After debating a concept, the designated agent executes the `create_idea` tool. The swarm must reach consensus before this action triggers, ensuring only vetted concepts hit your backlog.

One agent usually calls `get_feature` and shares the JSON response in the group chat. The other agents read that shared context and begin their deliberation based on the exact specifications.

Yes. The `McpToolAdapter` translates the Aha! API parameters into a format the AutoGen agents understand. You skip writing custom wrappers for every endpoint.

Vinkius provisions a stateless V8 Isolate Sandbox for every connection. Your acceptance criteria and roadmap timelines never persist on our infrastructure. Auth tokens stay encrypted.

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