How to Use the Atlassian (Jira & Confluence) MCP in AutoGen

Q: Can AutoGen agents query both Jira and Confluence simultaneously using this Atlassian (Jira & Confluence) setup?

Yes. You can assign getpage to a researcher agent and searchissues to a tracker agent, letting them share findings in their AutoGen chat history.

Let your AutoGen agents debate project specs and file Jira tickets based on Confluence requirements.

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Connect Atlassian (Jira & Confluence) MCP to AutoGen

Create your Vinkius account to connect Atlassian (Jira & Confluence) 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 Atlassian (Jira & Confluence) with AutoGen

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Multi-Agent Debates Over Jira Requirements

AutoGen agents can argue over ticket scope using live data. A product manager agent calls `get_page` to read requirements, while a developer agent calls `get_issue` to challenge the timeline. This MCP Server provides the raw data needed for these multi-agent negotiations. By accessing `list_projects`, agents establish a shared context before debating sprint allocations.

Consensus-Driven Sprint Planning with AutoGen

Automate sprint reviews by letting AutoGen agents analyze active boards. One agent pulls backlog issues using `search_issues`, while another reviews sprint capacity via `list_sprints`. The agents collaborate to propose the next sprint plan. Because they access the live API through this MCP Server, their recommendations are based on actual team capacity.

Automated Wiki Auditing via Collaborative Agents

Deploy a team of AutoGen agents to audit your Confluence workspaces. A scanner agent uses `list_spaces` to find outdated docs, and a writer agent uses `search_content` to verify references. This collaborative loop ensures your documentation stays accurate. The AutoGen agents resolve discrepancies by discussing findings in their chat thread before presenting a final report.

Setup guide

Set up Atlassian (Jira & Confluence) 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 Atlassian (Jira & Confluence) 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="Atlassian (Jira & Confluence)_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Atlassian (Jira & Confluence) 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="Atlassian (Jira & Confluence)_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Atlassian (Jira & Confluence) 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 Atlassian. 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 Atlassian (Jira & Confluence) MCP in AutoGen

Register the MCP tools with your AssistantAgent using the AutoGen MCP extension. The schema conversion for tools like `get_issue` happens automatically behind the scenes.

Yes. You can assign `get_page` to a researcher agent and `search_issues` to a tracker agent, letting them share findings in their AutoGen chat history.

Yes, the server connects via standard HTTP transport protocols. AutoGen handles the connection using its streamable server parameters.

Agents use their built-in reasoning to debate findings from `list_boards`. They continue negotiating until they reach a consensus on the project state.

Your Jira board metadata and Confluence credentials are never exposed. The Vinkius platform handles authentication securely, passing tokenized requests through sandboxed isolates that discard memory immediately after execution.

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