If you want an AI agent to work with Atlassian, you quickly hit a practical annoyance: Jira, Confluence and Bitbucket are three products, and the usual answer is three separate MCP servers with three configs to install and keep alive. I packaged them into one.
Repo: https://github.com/ahmet-ozel/atlassian-mcp-server
What it is
A single MCP (Model Context Protocol) server that exposes Jira, Confluence and Bitbucket (Server / Data Center) as 61 tools under one configuration. One install, one config, and any MCP client (Claude, custom agents, and so on) gets access to all three systems through a uniform tool interface. It is Python and MIT licensed.
Why one server instead of three
Running three servers means three processes to supervise, three sets of credentials to wire up, and three places for things to break. More subtly, an agent that needs to do real work often crosses product boundaries: read a Confluence page, open a Jira issue, link a Bitbucket pull request. When those tools live behind one server with consistent naming, the agent can chain them without you gluing three configs together.
The thing that actually gets hard: tool naming
With 61 tools in one place, the interesting problem is not the API calls, it is helping the model reliably pick the right tool. When you have create_issue, create_page, create_pull_request and a dozen search variants, naming and descriptions matter more than the underlying implementation. Clear, consistent, predictable tool names are what keep the model from calling the Confluence search when it meant the Jira one. This is the part I keep iterating on.
Server / Data Center focus
A lot of tooling assumes Atlassian Cloud. This targets Server and Data Center deployments, which are still everywhere in enterprises and often the environments where teams most want automation but have the fewest ready-made integrations.
Repo: https://github.com/ahmet-ozel/atlassian-mcp-server
If you use Atlassian Server or Data Center, I would like to know which tools are missing for your workflow. And for anyone building MCP servers with large tool counts: how do you structure tool names and descriptions so the model chooses correctly?
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