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

Q: Can Mastra AI handle creating reports from Atlassian (Jira & Confluence) data?

Yes. Your agent can run a searchissues query, loop through the results calling getissue for details, and then format that data into a report. Mastra's workflow engine ensures the process completes, even if some API calls are slow.

Q: Can I require human approval for certain Atlassian actions in Mastra AI?

Yes, Mastra supports requireToolApproval. You could build a workflow where an agent uses searchissues to find tickets to close, but it must ask for a human's OK before it proceeds with the action.

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Connect Atlassian (Jira & Confluence) MCP to Mastra AI

Create your Vinkius account to connect Atlassian (Jira & Confluence) to Mastra AI 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 Mastra AI

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Build Unbreakable Jira Automations

Mastra is for building workflows, and this server gives your workflows access to Jira. You can build an agent that finds a project with `list_projects`, then uses `search_issues` with JQL to find high-priority bugs that need attention. Here's the thing: if an API call fails, Mastra's engine automatically retries. This lets you build background jobs for things like daily reporting or issue triage that you can actually trust to run without constant supervision.

Create Self-Updating Documentation

Connect Jira events to Confluence actions. A Mastra agent can monitor a sprint using `list_sprints` and `search_issues` to see what's happening. It's a simple way to keep an eye on progress. When the sprint ends, the agent can automatically generate a summary. It uses `get_issue` to pull details for each completed task and then assembles a report. Your release notes practically write themselves.

Your Mastra AI Agent's Atlassian Tools

This MCP Server is the bridge between your Mastra agent and your company's project data. Your agent can find the right board with `list_boards`, check its own permissions with `get_myself`, or discover available knowledge spaces with `list_spaces`. You build the logic in your workflow, and these tools give your agent the context it needs to operate. It turns your agent from a simple script into a knowledgeable worker that can navigate your Atlassian environment.

Setup guide

Set up Atlassian (Jira & Confluence) MCP in Mastra AI

Prerequisites

Node.js 18+ and a TypeScript project
@mastra/mcp + @mastra/core packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run npm install @mastra/mcp @mastra/core plus your preferred model provider (e.g. @ai-sdk/openai).
2

Configure the MCPClient
Create an MCPClient with your Vinkius endpoint as a URL object. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Discover and inject tools
Call mcpClient.listTools() and spread the result into your agent's tools object. All Atlassian (Jira & Confluence) tools become native Mastra tools.
4

Run with any model
Swap openai("gpt-4o") for any AI SDK-compatible provider. Call agent.generate() and the agent routes tool calls through MCP automatically.

agent.ts

import { MCPClient } from "@mastra/mcp";
import { Agent } from "@mastra/core/agent";
import { openai } from "@ai-sdk/openai";

const mcpClient = new MCPClient({
  id: "atlassian-jira-confluence-mcp-client",
  servers: {
    "atlassian-jira-confluence-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

const agent = new Agent({
  name: "Atlassian (Jira & Confluence) Agent",
  model: openai("gpt-4o"),
  instructions: "You have access to Atlassian (Jira & Confluence) tools.",
  tools: {
    ...(await mcpClient.listTools()),
  },
});

const result = await agent.generate(
  "List recent Atlassian (Jira & Confluence) transactions"
);
console.log(result.text);

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Common questions about Atlassian (Jira & Confluence) MCP in Mastra AI

Use tools from this server in a Mastra workflow. Your agent can `search_issues` in Jira for a specific label, then use `get_page` to check a Confluence page and see if it needs to be updated.

Yes. Your agent can run a `search_issues` query, loop through the results calling `get_issue` for details, and then format that data into a report. Mastra's workflow engine ensures the process completes, even if some API calls are slow.

Mastra AI has built-in retry logic with exponential backoff. Your agent will automatically try the request again. This makes your automations far more resilient than a simple script.

Yes, Mastra supports `requireToolApproval`. You could build a workflow where an agent uses `search_issues` to find tickets to close, but it must ask for a human's OK before it proceeds with the action.

The server acts as a secure, stateless proxy. It uses your token to fetch Jira issue data or Confluence page content for a specific tool call and immediately forwards it to your agent. No data is ever stored on the Vinkius server.

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