Bitbucket MCP Server (Python)

Connect Claude Code, OpenAI Codex, Cursor, VS Code (GitHub Copilot), and any MCP-compatible AI assistant to your Bitbucket Cloud repositories. Review pull requests, monitor pipelines, and manage your code — all through natural language.

Features

• 60+ MCP tools — repositories, pull requests, comments, tasks, diffs, pipelines (runtime + config), build statuses, reviewers, draft PRs, batch review, issue tracker, commits, source/file browsing
• MCP 2025 tool annotations — every tool advertises readOnlyHint / destructiveHint / idempotentHint / openWorldHint + a human-readable title, so clients (Claude Code, Cursor) auto-include read-only tools and warn before destructive operations
• Slim responses — stripped API noise for lower LLM token usage
• Configurable — enable/disable tools via configs/tools.json or BITBUCKET_TOOLS_CONFIG env var
• Secure credentials — environment variables or system keychain

Quick Start

1. Install

The recommended way to run the server is via uvx (zero install, isolated environment):

# Always latest version
uvx --from bitbucket-mcp-py bitbucket-mcp

# Pin a specific version
uvx --from bitbucket-mcp-py==1.8.1 bitbucket-mcp

Why --from? The PyPI package is bitbucket-mcp-py but the command entry point is bitbucket-mcp. The --from flag tells uvx which package to install.

2. Configure credentials

Set the following environment variables (or use a .env file — see Credentials):

Get your API token at: https://id.atlassian.com/manage-profile/security/api-tokens

⚠️ Use a scoped token, not a global one. When creating the token, select specific scopes (e.g. Repositories: Read, Pull requests: Read/Write). Global tokens without explicit scopes do not work with this MCP server.

3. Configure your AI assistant

Claude Code (recommended)

Option A — CLI (fastest):

claude mcp add bitbucket-mcp \
  -e [email protected] \
  -e BITBUCKET_TOKEN=your-api-token \
  -e BITBUCKET_WORKSPACE=your-workspace \
  -- uvx --from bitbucket-mcp-py bitbucket-mcp

Option B — JSON config (~/.claude.json or project .mcp.json):

{
  "mcpServers": {
    "bitbucket-mcp": {
      "type": "stdio",
      "command": "uvx",
      "args": ["--from", "bitbucket-mcp-py", "bitbucket-mcp"],
      "env": {
        "BITBUCKET_USERNAME": "[email protected]",
        "BITBUCKET_TOKEN": "your-api-token",
        "BITBUCKET_WORKSPACE": "your-workspace"
      }
    }
  }
}

OpenAI Codex

Option A — CLI (fastest):

codex mcp add bitbucket-mcp \
  --env [email protected] \
  --env BITBUCKET_TOKEN=your-api-token \
  --env BITBUCKET_WORKSPACE=your-workspace \
  -- uvx --from bitbucket-mcp-py bitbucket-mcp

Option B — TOML config (~/.codex/config.toml):

[mcp_servers.bitbucket-mcp]
command = "uvx"
args = ["--from", "bitbucket-mcp-py", "bitbucket-mcp"]
env = { BITBUCKET_USERNAME = "[email protected]", BITBUCKET_TOKEN = "your-api-token", BITBUCKET_WORKSPACE = "your-workspace" }

Cursor

Add to ~/.cursor/mcp.json:

{
  "mcpServers": {
    "bitbucket-mcp": {
      "command": "uvx",
      "args": ["--from", "bitbucket-mcp-py", "bitbucket-mcp"],
      "env": {
        "BITBUCKET_USERNAME": "[email protected]",
        "BITBUCKET_TOKEN": "your-api-token",
        "BITBUCKET_WORKSPACE": "your-workspace"
      }
    }
  }
}

VS Code (GitHub Copilot)

Add to .vscode/mcp.json (workspace) or ~/Library/Application Support/Code/User/mcp.json (global, macOS):

{
  "servers": {
    "bitbucket-mcp": {
      "type": "stdio",
      "command": "uvx",
      "args": ["--from", "bitbucket-mcp-py", "bitbucket-mcp"],
      "env": {
        "BITBUCKET_USERNAME": "[email protected]",
        "BITBUCKET_TOKEN": "your-api-token",
        "BITBUCKET_WORKSPACE": "your-workspace"
      }
    }
  }
}

Available Tools

Disabled by default: merge_pull_request (safety), stop_pipeline (safety), get_pull_request_patch (git am format — not useful for AI review), convert_pull_request_to_draft (not supported by Bitbucket API), delete_issue (safety), delete_issue_comment (safety), add_commit_comment (write op), create_pipeline_variable / update_pipeline_variable / delete_pipeline_variable (write ops), create_pipeline_schedule / update_pipeline_schedule / delete_pipeline_schedule (write ops), delete_pipeline_cache (safety), create_environment / delete_environment / create_deployment_variable / update_deployment_variable / delete_deployment_variable (write ops), create_branch_restriction / update_branch_restriction / delete_branch_restriction (write ops). Enable in configs/tools.json.

Governance scopes — Branch restriction read tools need the repository scope (repository:admin may be required depending on repo config); the write tools need repository:admin. Workspace member/permission tools need the account scope. The /members endpoint lists users without a per-user permission (use list_workspace_permissions for roles).

Deployments scopes — the read tools (list_environments, get_environment, list_deployments, get_deployment, list_deployment_variables) need the deployment scope; the write tools need deployment:write. Bitbucket has no server-side filter for deployments by environment (BCLOUD-18729) — filter on the environment field of list_deployments instead. There is no update_environment tool: Bitbucket exposes no PUT for environments (only POST .../changes for locking).

Custom tool configuration

By default the server reads configs/tools.json bundled with the package. You can point to a custom file at runtime without rebuilding:

export BITBUCKET_TOOLS_CONFIG=/path/to/my-tools.json

Fallback chain (first match wins):

1. BITBUCKET_TOOLS_CONFIG environment variable
2. Built-in configs/tools.json

Fail-safe behaviour — If BITBUCKET_TOOLS_CONFIG is set but the file is missing or contains invalid JSON, the server raises an error on startup (explicit failure rather than silently ignoring the override). If the built-in default is missing, all tools are enabled.

Token tip — get_pull_request_diff accepts an optional path parameter to filter the diff to a single file, reducing token usage by ~95% on large PRs:

get_pull_request_diff(repo_slug, pull_request_id, path="src/services/myService.ts")

MCP Prompts

The server also exposes MCP Prompts — parameterised templates that compatible clients (Claude Code, Cursor, ...) surface as slash commands. Instead of remembering tool names, you invoke a prompt and the assistant orchestrates the right tools for you. They appear in the client's prompt picker (prompts/list).

Prompts are enabled/disabled in configs/tools.json under the top-level prompts key (separate from tools).

Credentials

Option 1: .env file (recommended)

cp .env.example .env
# Edit .env with your credentials

Option 2: System keychain (most secure)

pip install 'bitbucket-mcp-py[keyring]'
python3 -c "import keyring; keyring.set_password('bitbucket-mcp', 'bitbucket_token', 'YOUR_TOKEN')"

Docker (Alternative)

If you prefer running the server in a container:

docker build -t bitbucket-mcp-py .
docker run -d --name bitbucket-mcp --env-file .env bitbucket-mcp-py

Then configure your AI assistant to use docker exec:

{
  "mcpServers": {
    "bitbucket-mcp": {
      "command": "docker",
      "args": ["exec", "-i", "bitbucket-mcp", "python", "-m", "src.main", "--transport", "stdio"]
    }
  }
}

Transports

The server speaks stdio by default (the standard transport for local MCP clients). For a network deployment it also supports Streamable HTTP (MCP spec 2025-03-26):

# Streamable HTTP on 0.0.0.0:8080
python -m src.main --transport http --host 0.0.0.0 --port 8080

Clients connect to http://<host>:<port>/mcp (e.g. http://localhost:8080/mcp).

--transport sse (legacy Server-Sent Events) is still accepted but deprecated — it emits a DeprecationWarning. Prefer --transport http.

Development

# Install dev dependencies
uv sync --extra dev

# Run tests
uv run pytest tests/ -v

# Run specific test
uv run pytest tests/test_client.py -v

Requirements

• Python 3.12+
• Bitbucket API token

License

MIT

References

• MCP Registry — Official MCP server registry
• PyPI Package — Python package
• MCP Python SDK
• Bitbucket API 2.0
• FastMCP Framework