semvec/pypi-packages huggingface dataset:
bitbucket-mcp-py 1.4.1
MCP Server for Bitbucket API - Container optimized with configurable tools, structured output and slim responses
Requires Python: >=3.12
Keywords: api, bitbucket, mcp, mcp-server, model-context-protocol

# Bitbucket MCP Server (Python)

<!-- mcp-name: io.github.lawp09/bitbucket-mcp -->

A Model Context Protocol (MCP) server for Bitbucket API, optimized for container execution.

## Features

- Basic Auth (`base64(email:token)`)
- Container-ready (Podman/Docker)
- 22 MCP tools (repositories, PRs, comments, pipelines)
- Configurable tools via `configs/tools.json`
- Optional keychain support for secure credentials

## Quick Start

### 1. Configure Credentials

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

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

### 2. Build & Run

```bash
make build
make up
make verify  # Test authentication
```

### 3. Configure Claude Desktop

Add to `claude_desktop_config.json`:

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

### 4. Configure VS Code (GitHub Copilot)

Add to `~/Library/Application Support/Code/User/mcp.json`:

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

## Available Tools

| Category | Tools |
|----------|-------|
| **Repositories** | `list_repositories`, `get_repository` |
| **Pull Requests** | `get_pull_requests`, `get_pull_request`, `create_pull_request`, `update_pull_request`, `approve_pull_request`, `unapprove_pull_request`, `decline_pull_request` |
| **Comments** | `get_pull_request_comments`, `add_pull_request_comment`, `get_pull_request_activity`, `get_pull_request_diff`, `get_pull_request_commits` |
| **Build Status** | `get_pull_request_statuses`, `get_pull_request_diffstat` |
| **Pipelines** | `list_pipeline_runs`, `get_pipeline_run`, `get_pipeline_steps`, `get_pipeline_step_logs` |

## Make Commands

```bash
make build    # Build container
make up       # Start container
make down     # Stop container
make test     # Run tests
make verify   # Test Bitbucket auth
make logs     # View logs
make clean    # Remove container/image
```

## Secure Credentials (Optional)

For enhanced security, use system keychain instead of environment variables:

```bash
# Install keyring support
pip install 'bitbucket-mcp-py[keyring]'

# Store in macOS Keychain
python3 -c "import keyring; keyring.set_password('bitbucket-mcp', 'bitbucket_token', 'YOUR_TOKEN')"
```

## Development

```bash
# Run tests
make test

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

## Requirements

- Python 3.12+
- Podman or Docker
- Bitbucket API token

## License

MIT

## References

- [MCP Python SDK](https://github.com/modelcontextprotocol/python-sdk)
- [Bitbucket API 2.0](https://developer.atlassian.com/cloud/bitbucket/rest/api-group-pullrequests/)
- [FastMCP Framework](https://gofastmcp.com/)
regfans 0.0.7
Regular fans of vector configurations
Requires Python: >=3.9

![Fan flip graph](images/fan_flip_graph.png)

(This has a lot of overlap with TOPCOM which will be quicker than existing methods. This repo will progressive port more of the computations over to TOPCOM (via [triangulumancer](https://github.com/ariostas/triangulumancer))

# regfans
Software for studying vector configurations defined over the lattice vectors. This includes
- constructing regular triangulations (i.e., polyhedral fans) of such vector configurations via lifting,
- constructing all (regular) triangulations via computation of flip graphs,
- verification of various properties of the vector configuration/fan, and
- efficient linear flipping.

See [Triangulations: Structures for Algorithms and Applications](https://link.springer.com/book/10.1007/978-3-642-12971-1) by De Loera, Rambau, and Santos for a definitive resource on such topics.

This package, `regfans`, was originally developed for constructing toric varieties in the work [Calabi-Yau Threefolds from Vex Triangulations](https://arxiv.org/abs/2512.14817). Said work was supported in part by NSF grant PHY-2309456. All toric-geometric computations are isolated to [CYTools](https://github.com/LiamMcAllisterGroup/cytools), which has an extension `vector_config` building off of `regfans`.

## Installation
`regfans` can be installed using either conda or pip. To install `regfans` using conda, please see/use the provided `environment.yml` file:
```
conda env create -f environment.yml
conda activate regfans
```
To install `regfans` using pip, either run (to install the most recent release; also see [PyPI listing](https://pypi.org/project/regfans/))
```
pip install regfans
```
or (to install a local version)
```
pip install .
```
N.B.: many methods in `regfans` require computation of dual cones (i.e., the generators of a cone defined via hyperplanes or vice-versa). Currently, this requires [pplpy](https://pypi.org/project/pplpy/) which cannot be automatically installed via pip.

## API

See [api.md](documentation/api.md) for full API reference.

(To update documentation, just run `pydoc-markdown; py documentation/clean_api.py`)

## Tutorials

See the [tutorials directory](tutorials/) for some commented example scripts showing how to construct a vector configuration, check properties of it, construct fans from it, and check the properties of said fans.