MCP Bitbucket Dc

Atlassian Data Center MCP

The leading MCP for Atlassian Data Center, built for AI-first companies that run Jira, Confluence and Bitbucket on their own servers.

Your teams chose Data Center for control over their data. They shouldn't have to give up the best AI models to keep it. Three Model Context Protocol (MCP) servers let Claude, Cursor and any other MCP client work in your self-hosted Atlassian tools: search and update issues, read and write pages, review pull requests, without flooding the model's context. Each is one npx command. Your agent signs in with your personal access token, so it can do what you can, and nothing more.

npx -y mcp-jira-dc setup                    # ← saves your Jira address and token

claude mcp add jira -- npx -y mcp-jira-dc # ← connects Claude Code

Claude now sees 14 Jira tools: the everyday ones for issues, comments and transitions, plus a search that reaches 43 more when it needs them, and a sandbox where it can combine many reads into one answer.

A community project. Not affiliated with or endorsed by Atlassian. Jira, Confluence and Bitbucket are trademarks of

Atlassian.

Why you'll like it

• One command, nothing to host: npx runs each server on your machine. No Python, no Docker, no server to run. Built for Data Center and Server, the editions Atlassian's own MCP server doesn't cover.
• A small tool list, with nothing out of reach: every listed tool costs tokens in every request, so the everyday tools are listed and the rest wait behind a search. The agent sees 14 Jira tools instead of 61, and can still use all of them.
• Answers, not pages of JSON: for a question that takes many calls, the agent writes a short script that makes the calls in a sandbox and returns only the answer. One example below returns 226 characters instead of 1,215.
• Only the tools you can use: at startup the Jira server reads your permissions and hides what your account could never run.
• Writes stay visible: every tool tells the client whether it reads, changes or deletes, so the client can ask you first. Scripts can only read, and deleting always needs a setting you turn on.
• An audit trail when you need one: turn on the audit log to record every tool the agent calls, including the ones it reaches through the search or from a script, without recording what it read or wrote.
• Locked down when you need it: read-only mode, an exact tool allowlist, merges only on the repositories and target branches you name, and attachments only inside folders you name.
• Credentials in the right place: setup checks your token against the server, then stores it in your OS keychain (macOS Keychain, Windows Credential Manager, or the Linux desktop keyring), or in a file only your user can read. The token is masked in everything the servers write.

Why run it on your machine?

Data Center usually lives behind a firewall or a VPN. A server that runs where you work reaches it the way your browser does.

Atlassian's official MCP server is a hosted server for Atlassian Cloud. These servers are for Data Center and Server.

The three servers

Install only the ones you use. They share one configuration format, so a single settings file can serve all three.

Supported Jira apps

The Jira server also has tools for these Marketplace apps:

Beta means built and checked, but not yet run against a live instance of the app. Supported apps lists the exact versions each app's tools were built from and how they were checked.

Install

npx -y mcp-jira-dc setup

Requires Node.js 22.22.2 or later, a Data Center or Server instance, and a personal access token. Swap in mcp-confluence-dc or mcp-bitbucket-dc for the other servers.

Crash course

1. Save your address and token

setup asks for your host and token, makes one authenticated request to check them, and saves them:

npx -y mcp-jira-dc setup

To create a token in Jira or Confluence, open your profile picture, then Personal Access Tokens > Create token. In Bitbucket it's Manage account > HTTP access tokens. In scripts and CI, setup --non-interactive takes everything as flags and exits non-zero when the token doesn't work.

2. Connect your agent

install registers the server with Claude Code or Claude Desktop for you:

npx -y mcp-jira-dc install --claude-code      # runs claude mcp add

npx -y mcp-jira-dc install --claude-desktop # edits Claude Desktop's config, after backing it up

Cursor, VS Code and other clients with an mcpServers file:

{

"mcpServers": { "jira": { "command": "npx", "args": ["-y", "mcp-jira-dc"] }, "confluence": { "command": "npx", "args": ["-y", "mcp-confluence-dc"] }, "bitbucket": { "command": "npx", "args": ["-y", "mcp-bitbucket-dc"] } } }

Then ask something like *"Which issues in our current sprint are blocked, and who owns them?"*

3. Choose what's listed

Every setting is optional. Unset, Jira and Confluence list their everyday tools and offer the rest through the search.

Each server's README lists its toolsets and the settings that turn on risky tools: Jira, Confluence, Bitbucket.

4. Check your setup

doctor checks each step in turn and says how to fix what's wrong. It starts the server the way your MCP client does, so it shows exactly what the agent will get:

$ npx -y mcp-jira-dc doctor

mcp-jira-dc doctor

✓ Node.js 22.22.2 ✓ Settings everything needed is set JIRA_HOST: jira.example.com, from C:\Users\alice\.atlassian-dc-mcp\jira.env JIRA_API_BASE_PATH: (not set) JIRA_API_TOKEN: ••••HcU1, from Windows Credential Manager JIRA_DEFAULT_PAGE_SIZE: 25 (default) ✓ Keychain token kept in Windows Credential Manager ✓ Sign-in signed in as Alice Smith (alice) ✓ Tools 11 listed, about 2,900 tokens of context 32 more through jira_searchTools jira_runCode is on server: Permission filter hid 14 Jira tools: jira_transitionIssue, jira_linkIssues, ...

The token is masked, so the output is safe to paste into a bug report.

What the agent sees

These are real responses from the Jira server, run against a test Jira. The tool list holds the everyday tools and four more:

jira_searchIssues, jira_getIssue, jira_getIssueComments, jira_createIssue, jira_updateIssue,

jira_postIssueComment, jira_updateIssueComment, jira_getTransitions, jira_transitionIssue, jira_downloadAttachment, jira_searchTools, jira_runReadTool, jira_runWriteTool, jira_runCode

Asked about a sprint, the agent searches for a tool. jira_searchTools returns the matches with their input schemas (left out here), and says whether each one reads or writes:

[

{ "name": "jira_getSprintIssues", "toolset": "jira_agile", "access": "read", "description": "List the issues in a sprint in the JIRA Data Center edition instance, in rank order, optionally narrowed by JQL. Returns key, summary, status, type, priority and assignee per issue." }, { "name": "jira_addIssuesToSprint", "toolset": "jira_agile", "access": "write", "description": "Move issues into a future or active sprint in the JIRA Data Center edition instance, taking them out of any other sprint. At most 50 issues per call." }, { "name": "jira_moveIssuesToBacklog", "toolset": "jira_agile", "access": "write", "description": "Move issues to the backlog in the JIRA Data Center edition instance, removing them from their future or active sprint. At most 50 issues per call." } ]

It can run the read tool with jira_runReadTool, or, to answer the question in one step, write a script for jira_runCode:

const { issues } = await tools.jira_getSprintIssues({ sprintId: 42 });

const blocked = issues.filter((issue) => issue.status === 'Blocked'); console.log(${blocked.length} of ${issues.length} issues are blocked); return blocked.map((issue) => ${issue.key} ${issue.summary} (${issue.assignee?.displayName ?? 'unassigned'}));

{

"result": [ "PAY-102 Migrate payouts to new ledger (Ben Okafor)", "PAY-104 Upgrade payment SDK (Amira Haddad)", "PAY-106 Refund API returns 500 on partial refunds (Ben Okafor)" ], "logs": ["3 of 7 issues are blocked"], "toolCalls": 1 }

That answer is 226 characters. The full sprint listing it came from is 1,215, and on a real sprint the gap grows with every