semvec/pypi-packages huggingface dataset:
lijunshi-demo 0.1.0
lijunshi demo pack
Requires Python: >=3.11

# lijunshi-demo MCP server

lijunshi demo pack

## Components

### Resources

The server implements a simple note storage system with:
- Custom note:// URI scheme for accessing individual notes
- Each note resource has a name, description and text/plain mimetype

### Prompts

The server provides a single prompt:
- summarize-notes: Creates summaries of all stored notes
  - Optional "style" argument to control detail level (brief/detailed)
  - Generates prompt combining all current notes with style preference

### Tools

The server implements one tool:
- add-note: Adds a new note to the server
  - Takes "name" and "content" as required string arguments
  - Updates server state and notifies clients of resource changes

## Configuration

[TODO: Add configuration details specific to your implementation]

## Quickstart

### Install

#### Claude Desktop

On MacOS: `~/Library/Application\ Support/Claude/claude_desktop_config.json`
On Windows: `%APPDATA%/Claude/claude_desktop_config.json`

<details>
  <summary>Development/Unpublished Servers Configuration</summary>
  ```
  "mcpServers": {
    "lijunshi-demo": {
      "command": "uv",
      "args": [
        "--directory",
        "D:\code\mcp_servers\files\lijunshi-demo",
        "run",
        "lijunshi-demo"
      ]
    }
  }
  ```
</details>

<details>
  <summary>Published Servers Configuration</summary>
  ```
  "mcpServers": {
    "lijunshi-demo": {
      "command": "uvx",
      "args": [
        "lijunshi-demo"
      ]
    }
  }
  ```
</details>

## Development

### Building and Publishing

To prepare the package for distribution:

1. Sync dependencies and update lockfile:
```bash
uv sync
```

2. Build package distributions:
```bash
uv build
```

This will create source and wheel distributions in the `dist/` directory.

3. Publish to PyPI:
```bash
uv publish
```

Note: You'll need to set PyPI credentials via environment variables or command flags:
- Token: `--token` or `UV_PUBLISH_TOKEN`
- Or username/password: `--username`/`UV_PUBLISH_USERNAME` and `--password`/`UV_PUBLISH_PASSWORD`

### Debugging

Since MCP servers run over stdio, debugging can be challenging. For the best debugging
experience, we strongly recommend using the [MCP Inspector](https://github.com/modelcontextprotocol/inspector).


You can launch the MCP Inspector via [`npm`](https://docs.npmjs.com/downloading-and-installing-node-js-and-npm) with this command:

```bash
npx @modelcontextprotocol/inspector uv --directory D:\code\mcp_servers\files\lijunshi-demo run lijunshi-demo
```


Upon launching, the Inspector will display a URL that you can access in your browser to begin debugging.
flowpulse 2.0.6
A package to generate flow metrics charts and run Monte Carlo Simulation based Forecasts based on queries against Jira or Azure DevOps.
Requires Python: >=3.10

# flowpulse
![CI Workflow](https://github.com/letpeoplework/flowpulse/actions/workflows/publish.yml/badge.svg)
![PyPI - Downloads](https://img.shields.io/pypi/dm/flowpulse)

This python package allows you to visualize your measures of flow as well as to run Monte Carlo Simulations based on the data in your Jira or Azure DevOps Backlog. All it needs is a connection to your [Jira](https://confluence.atlassian.com/enterprise/using-personal-access-tokens-1026032365.html) or [Azure DevOps](https://learn.microsoft.com/en-us/azure/devops/organizations/accounts/use-personal-access-tokens-to-authenticate?view=azure-devops&tabs=Windows) and the queries to your backlog. Feel free to check out the code, propose improvements and also make it your own by adjusting it to your context and potentially integrating it into some kind of pipeline of yours. The true power of Flow Metrics comes when inspected on a regular base. The point of collecting data is to take action, so use this to make informed decisions about what you want to adjust! You can use this for free, hope it helps.

This tool is provided for free by [LetPeopleWork](https://letpeople.work). If you are curious about Flow Metrics, Kanban, #NoEstimates etc., feel free to reach out to us via [Slack](https://join.slack.com/t/let-people-work/shared_invite/zt-2y0zfim85-qhbgt8N0yw90G1P~JWXvlg)

# Documentation
You can find the full documentation under [https://docs.flowpulse.letpeople.work](https://docs.flowpulse.letpeople.work).