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

Q: Can I restrict which Atlassian (Jira & Confluence) tools Google ADK exposes?

You absolutely can. Pass the optional toolnames list to your McpToolset configuration. This lets you hide administrative tools and only expose specific read queries to the Gemini model.

Feed Atlassian (Jira & Confluence) data into Google ADK to give your Gemini agents massive context windows for enterprise project analysis.

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Connect Atlassian (Jira & Confluence) MCP to Google ADK

Create your Vinkius account to connect Atlassian (Jira & Confluence) to Google ADK 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 Google ADK

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Massive Wiki Context for Gemini

Gemini models ingest entire documentation trees by executing `search_content` against your Confluence instance. The Google ADK handles the data transfer, pulling massive CQL result sets directly into the 1M+ token context window. Finding specific structural data requires mapping the workspace. Your agent calls `list_spaces` to find the relevant department, followed by `get_page` to extract the raw rich text for long-form reasoning tasks.

JQL Execution via MCP Server

Running `search_issues` lets your Gemini agent pull hundreds of tickets at once using native JQL. You cross-reference these bug reports with your existing BigQuery datasets to find patterns in production failures. Narrowing down a specific problem requires exact details. The agent executes `get_issue` to read the full description, comments, and status of a single ticket before generating an analysis report.

Map Enterprise Agile Boards

Discovering active work streams starts with the `list_boards` tool. The agent grabs the internal board IDs, which you can filter down using the tool_names parameter if you only want to expose specific read operations. Tracking iteration progress happens through `list_sprints`. Once the agent knows the board ID, it fetches the current sprint boundaries to understand timeline constraints for your Vertex AI pipelines.

Setup guide

Set up Atlassian (Jira & Confluence) MCP in Google ADK

Prerequisites

Python 3.10+ installed
google-adk package (pip install google-adk)
Active Vinkius subscription with a valid endpoint token

1

Install Google ADK

Run pip install google-adk to install the Agent Development Kit. MCP support is included via the McpToolset class.
2

Connect via SSE transport

Use McpToolset.from_server() with SseServerParams pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create an LlmAgent

Pass the returned mcp_tools list directly to LlmAgent(tools=mcp_tools). The ADK maps each MCP tool to a native Gemini function call — no manual schema definitions required.
4

Run with any Gemini model

The agent works with any Gemini model (gemini-2.0-flash, gemini-2.5-pro, etc.). Copy the full example on the right to get started with Atlassian (Jira & Confluence) tools in your ADK agent.

agent.py

from google.adk.agents import LlmAgent
from google.adk.tools.mcp_tool.mcp_toolset import McpToolset
from google.adk.tools.mcp_tool.mcp_session_manager import SseServerParams

# Connect to the MCP via SSE
mcp_tools, exit_stack = await McpToolset.from_server(
    connection_params=SseServerParams(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    )
)

# Create your agent with auto-discovered tools
agent = LlmAgent(
    name="Atlassian (Jira & Confluence)_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Atlassian (Jira & Confluence) tools via MCP.",
    tools=mcp_tools,
)

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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 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 Google ADK

Run pip install google-adk in your environment. Initialize the connection using McpToolset(server_params=StreamableHttpServerParameters(url="...")). Pass this toolset to your LlmAgent constructor.

You absolutely can. Pass the optional tool_names list to your McpToolset configuration. This lets you hide administrative tools and only expose specific read queries to the Gemini model.

Vinkius provides a Streamable HTTP URL that works natively with Google ADK. You just paste the endpoint into your server parameters and the agent handles the rest.

The massive context window of Gemini 1.5 Pro easily absorbs multiple long wiki pages. You can pull dozens of pages in a single session without hitting token limits.

Your proprietary bug reports and internal wiki text pass through a strict V8 Isolate Sandbox. Vinkius operates on a zero-trust model where the container exists only for the duration of the request. Authentication is handled upstream, keeping your credentials off the agent.

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