● To build and use your package with uv:

  1. Build your package:
  uv pip install build
  python -m build

  2. Install locally with uv:
  uv pip install dist/mcp_ntopng-0.1.0-py3-none-any.whl

  3. Configure Claude Desktop:
    - Edit your configuration file:
        - MacOS: ~/Library/Application Support/Claude/claude_desktop_config.json
      - Windows: %APPDATA%/Claude/claude_desktop_config.json
  4. Add this configuration:
  {
    "mcpServers": {
      "ntopng": {
        "command": "uv",
        "args": ["run", "mcp-ntopng"],
        "env": {
          "NTOPNG_HOST": "<ntopng-host>",
          "NTOPNG_DBPORT": "<ntopng-dbport>",
          "NTOPNG_DBUSER": "<ntopng-dbuser>",
          "NTOPNG_DBPASSWORD": "<ntopng-dbpassword>",
          "NTOPNG_SECURE": "true",
          "NTOPNG_VERIFY": "true",
          "NTOPNG_CONNECT_TIMEOUT": "30",
          "NTOPNG_SEND_RECEIVE_TIMEOUT": "300",
          "NTOPNG_API_KEY": "your-ntopng-token"
        }
      }
    }
  }

  This configuration uses uv run to execute your package script. After restarting Claude Desktop, it should be able to use your ntopng MCP
  server.


==============================================

● Plan for publishing mcp-ntopng as a package on PyPI:

🎉 Awesome — glad it worked!

Publishing your MCP Server package (`mcp-ntopng`) to **PyPI** is a great next step! You’ll be able to:

- `pip install mcp-ntopng`
- Run it with `mcp-ntopng` directly from any terminal
- Use it in Claude Desktop via a simple path to the installed script

---

## ✅ Step-by-Step: Publish to PyPI

### 1. ✅ Clean up `pyproject.toml`

Make sure it includes the essentials:

```toml
[project]
name = "mcp-ntopng"                     # Must match the name you'll use on PyPI
version = "0.1.0"
description = "MCP server for ntopng integration"
authors = [{ name = "Your Name", email = "you@example.com" }]
license = "MIT"
readme = "README.md"
requires-python = ">=3.8"

dependencies = [
  "clickhouse-driver>=0.2.5",
  "mcp[cli]>=1.3.0",
  "requests>=2.32.3",
  "python-dotenv>=1.0.1",
  "pip-system-certs>=4.0"
]

[project.scripts]
mcp-ntopng = "mcp_ntopng.main:main"     # 👈 this makes the CLI available

[build-system]
requires = ["hatchling"]
build-backend = "hatchling.build"
```

---

### 2. ✅ Register on PyPI

- Go to: https://pypi.org/account/register/
- Create an account if you haven’t already
- Save your **username** and **password / token**

---

### 3. ✅ Build the package

```bash
uv build
```

(or use `python -m build` if using standard tooling)

---

### 4. ✅ Upload to PyPI

First, install Twine:

```bash
uv pip install twine
```

Then upload:

```bash
twine upload dist/*
```

It will prompt for your username and password/token.

> 🔐 You can also use an [API token](https://pypi.org/help/#apitoken) instead of password for more security.

---

### 5. ✅ Test the package

From a clean environment or container:

```bash
pip install mcp-ntopng
mcp-ntopng  # 🎉 should just work!
```

---

## 🛠 Bonus: Private PyPI or GitHub Releases?

If you don’t want to publish publicly:
- Use a **private PyPI index** (e.g. Artifactory, Cloudsmith)
- Or publish the `.whl` as a **GitHub Release** and let users `pip install` from a GitHub URL:
  ```bash
  pip install https://github.com/your-org/mcp-ntopng/releases/download/v0.1.0/mcp_ntopng-0.1.0-py3-none-any.whl
  ```

------------------------------------------------------------------------
Alternative:

  1. Update Package Metadata
    - Complete the missing items in the README.md:
        - Fill in the empty bullet points in the "Works with Claude desktop app" section
      - Update any outdated instructions
  2. Prepare Package Files for PyPI
    - Create a MANIFEST.in file to include non-Python files if needed
    - Add README files with installation and usage instructions
    - Create a proper entry point in main.py that correctly imports from mcp_ntopng.mcp_server
    - Verify pyproject.toml has all necessary classifiers (fix "Intended Audience :: Analysts" to use proper trove classifier format)
  3. Testing
    - Test package installation locally using pip install -e .
    - Test with different Python versions (verify 3.13 requirement)
    - Verify all dependencies are correctly specified in pyproject.toml
    - Test the package in isolated environments to ensure it works as expected
  4. Documentation
    - Add detailed documentation on supported MCP tools
    - Complete tool descriptions in the README.md
    - Document expected environment variables and configuration options
    - Add examples of usage with Claude
  5. PyPI Account and Publishing
    - Create an account on PyPI if not already done
    - Consider creating a test package on TestPyPI first
    - Generate distribution packages:
    python -m pip install --upgrade build
  python -m build
    - Upload to PyPI:
    python -m pip install --upgrade twine
  python -m twine upload dist/*
  6. Post-Release
    - Update the README.md to provide proper installation instructions via PyPI
    - Update the "Configuration" section in README.md with the PyPI installation method
    - Set up versioning strategy for future updates
    - Consider adding GitHub Actions for automated testing and publishing
  7. Security Considerations
    - Review code to ensure no hardcoded credentials
    - Validate that sensitive data is handled securely
    - Ensure that API keys and credentials are properly managed via environment variables
  8. Recommendations
    - Add proper docstrings to all modules and functions
    - Consider adding tests directory with unit/integration tests
    - Add a CHANGELOG.md file to track version changes
    - Add contribution guidelines if you expect community involvement

  This package appears to implement an MCP (Model Context Protocol) Server for ntopng, allowing AI agents like Claude to query network
  monitoring data using the ntopng database. The code looks solid but needs a few tweaks before being ready for PyPI publication.


