Mcp Server Wrapper Bitbucket

Atlassian MCP Servers

Native asynchronous Python MCP servers for Bitbucket Server/Data Center, Jira Cloud, and Confluence Cloud. Each server talks directly to its product's REST API and can be installed and run independently.

Packages

Package	API	PyPI name	Directory
Bitbucket	Server/Data Center REST API v1.0	bitbucket-mcp-atlassian	repository root
Jira	Cloud REST API v3 / Server-DC REST API v2	jira-mcp-atlassian	jira-mcp/
Confluence	Cloud REST API v2 / Server-DC REST API v2	confluence-mcp-atlassian	confluence-mcp/

The Jira and Confluence packages are self-contained projects with their own pyproject.toml, tests, environment example, and release metadata.

Quick start

Install uv, configure the package you want to use, and run its MCP entrypoint.

Bitbucket Server/Data Center

cp .env.example .env
uv sync
uv run bitbucket-mcp

Set BITBUCKET_URL and either BITBUCKET_TOKEN or both BITBUCKET_USERNAME and BITBUCKET_PASSWORD.

Jira Cloud

cd jira-mcp
cp .env.example .env
uv sync
uv run jira-mcp

Set JIRA_URL, JIRA_EMAIL, and JIRA_TOKEN. The token is an Atlassian API token associated with the email address.

Confluence Cloud

cd confluence-mcp
cp .env.example .env
uv sync
uv run confluence-mcp

Set CONFLUENCE_URL, CONFLUENCE_EMAIL, and CONFLUENCE_TOKEN.

Each server supports stdio, sse, and streamable-http transports through its corresponding *_TRANSPORT setting. TLS verification, timeout, and retry behavior are configurable through the environment files.

MCP configuration examples

{
  "mcpServers": {
    "bitbucket": {
      "command": "uv",
      "args": ["run", "--directory", "/absolute/path/to/this-repo", "bitbucket-mcp"],
      "env": {
        "BITBUCKET_URL": "https://bitbucket.example.com",
        "BITBUCKET_TOKEN": "your-token"
      }
    },
    "jira": {
      "command": "uv",
      "args": ["run", "--directory", "/absolute/path/to/this-repo/jira-mcp", "jira-mcp"],
      "env": {
        "JIRA_URL": "https://your-domain.atlassian.net",
        "JIRA_EMAIL": "[email protected]",
        "JIRA_TOKEN": "your-token"
      }
    },
    "confluence": {
      "command": "uv",
      "args": ["run", "--directory", "/absolute/path/to/this-repo/confluence-mcp", "confluence-mcp"],
      "env": {
        "CONFLUENCE_URL": "https://your-domain.atlassian.net",
        "CONFLUENCE_EMAIL": "[email protected]",
        "CONFLUENCE_TOKEN": "your-token"
      }
    }
  }
}

Capabilities

Bitbucket provides projects, repositories, pull requests, comments, reviews, diffs, branches, commits, files, users, permissions, and build-status tools.

Jira provides JQL issue search, issue retrieval and updates, issue creation/deletion, comments, transitions, assignments, projects, and issue types. Cloud uses ADF for rich text; Server/Data Center uses Jira wiki markup.

Confluence provides page listing, retrieval, creation, updates, deletion, spaces, child pages, labels, and comments on both Cloud and Server/Data Center.

Development and testing

Run the existing Bitbucket tests from the repository root:

uv run pytest

Run the independent Atlassian Cloud test suites from their package directories:

cd confluence-mcp && uv run pytest
cd ../jira-mcp && uv run pytest

All tests use mocked HTTP transports and do not contact Atlassian services.

Build and publish

Build an individual package from its directory:

cd jira-mcp
uv build

The root release workflow publishes packages through PyPI trusted publishing when matching tags are pushed:

git tag jira-v0.1.0
git push origin jira-v0.1.0

git tag confluence-v0.1.0
git push origin confluence-v0.1.0

Configure the pypi GitHub environment and trusted publishers for the repository before pushing release tags. Each release workflow uses PyPI's short-lived OIDC credential; no long-lived PyPI token is stored in GitHub.

Architecture

src/bitbucket_mcp/             Bitbucket Server/Data Center implementation
jira-mcp/src/jira_mcp/          Jira Cloud implementation
confluence-mcp/src/confluence_mcp/  Confluence Cloud implementation

Each package follows the same structure:
  client.py   async HTTP and authentication infrastructure
  config.py   pydantic-settings environment configuration
  tools.py    REST API capability functions
  server.py   FastMCP tool registration and transport entrypoint