semvec/pypi-packages huggingface dataset:
joplin-mcp-server 0.1.3
A Model Context Protocol (MCP) stdio server for Joplin, exposing Joplin's local REST API as MCP tools. Built with Python, uv, httpx, and FastMCP.
Requires Python: <3.15,>=3.12
Keywords: joplin, mcp, server, rest api, notes, python

# joplin-mcp-server

A Model Context Protocol (MCP) stdio server for Joplin, exposing Joplin's local REST API as MCP tools.

- **Tech stack:** Python, uv, httpx, FastMCP
- **License:** MIT

## Features
- Bridges Joplin's REST API to MCP tools
- Designed for extensibility and integration

## Quick Start
1. Clone this repository
2. Set up a Python 3.12+ environment (recommended: `uv`)
3. Install dependencies: `uv sync`
4. Run the server: `uvx joplin-mcp-server`

## Project Structure
- `src/joplin_mcp_server/` — Main package code
- `specs/` — API documentation and references

## License
MIT
agent-dev-cli 0.0.1b260205
AI Toolkit - CLI and SDK for agent debugging and workflow visualization
Requires Python: >=3.10.0
Keywords: ai, agent, toolkit, debugging, workflow, visualization

# Agent Dev CLI

A Python CLI and SDK for agent debugging and workflow visualization with VS Code AI Toolkit integration.

## Installation

```bash
pip install agent-dev-cli --pre
```

> **Note**: This package is currently in beta. The `--pre` flag is required to install pre-release versions.

## Usage

### Option 1: CLI Wrapper (Recommended)

The easiest way to use Agent Dev CLI - **no code changes required**:

```bash
# Run your agent script with agentdev instrumentation
agentdev run my_agent.py

# Specify a custom port
agentdev run workflow.py --port 9000

# Enable verbose output
agentdev run my_agent.py --verbose

# Pass arguments to your script
agentdev run my_agent.py -- --model gpt-4 --temperature 0.7
```

The CLI automatically:
- Intercepts `from_agent_framework()` calls
- Injects agentdev visualization endpoints
- Opens the workflow visualization in VS Code

### Option 2: Programmatic API

If you prefer explicit control, you can integrate agentdev directly:

```python
from agentdev import configure_agent_server
from azure.ai.agentserver.agentframework import from_agent_framework

# Create your agent
agent = build_agent(chat_client)

# Create agent server
agent_server = from_agent_framework(agent)

# Setup workflow visualization
configure_agent_server(agent_server)

# Run the server
await agent_server.run_async()
```

## CLI Commands

### `agentdev run`

Run a Python agent script with agentdev instrumentation.

```
agentdev run [OPTIONS] SCRIPT [ARGS]...

Options:
  -p, --port INTEGER    Agent server port (default: 8087)
  -v, --verbose         Enable verbose output
  --help                Show this message and exit
```

### `agentdev info`

Show agentdev configuration and status information.

```
agentdev info
```

## Features

- **Health Check Endpoint**: Adds a `/agentdev/health` endpoint to your agent server
- **Workflow Visualization**: Starts a visualization server on port 8090 for WorkflowAgent instances
- **Easy Integration**: Simple one-function setup

## Testing Your Agent Server

Once your agent server is running, you can test it using curl:

```bash
# Health check
curl http://localhost:8087/agentdev/health

# Send a request to your agent (streaming)
curl 'http://localhost:8087/agentdev/v1/responses' \
  -H 'Content-Type: application/json' \
  -d '{"model":"your-agent-model-id","input":{"role":"user","text":"Hello!"},"stream":true}'
```

## Requirements

- Python 3.10+
- [VS Code AI Toolkit](https://marketplace.visualstudio.com/items?itemName=ms-windows-ai-studio.windows-ai-studio) (for visualization)

## License

This project is licensed under the **Microsoft AI Toolkit – Agent Dev CLI License Terms**.
tsyganenkomodels-jl 0.1.0
Python wrapper for TsyganenkoModels.jl - access Tsyganenko magnetic field models via Julia
Requires Python: >=3.9
Keywords: tsyganenko, julia, space-physics, geomagnetism

# TsyganenkoModels

Python wrapper for [TsyganenkoModels.jl](https://github.com/JuliaSpacePhysics/TsyganenkoModels.jl) - evaluate Tsyganenko external magnetic field models from Python.

## Installation

```bash
pip install tsyganenkomodels-jl
```

The Julia package installs automatically on first use.

## Usage

```python
import tsyganenkomodels as tm
import datetime

# Compute the dipole tilt angle (radians)
ps = tm.dipole_tilt(datetime.datetime(2001, 1, 1, 2, 3, 4))

# Instantiate a model and evaluate GSM field (nT)
model = tm.T96(pdyn=2.0, dst=-87.0, byimf=2.0, bzimf=-5.0)
b = model([-5.1, 0.3, 2.8], ps)
```

## API

Available constructors/functions mirror the Julia package:

- `T89(iopt)`
- `T96(pdyn, dst, byimf, bzimf)`
- `T01(pdyn, dst, byimf, bzimf, g1=0.0, g2=0.0)`
- `TS04(pdyn, dst, byimf, bzimf, g1, g2, g3, w1, w2, w3, w4, w5, w6)`
- `t89`, `t96`, `t01`, `ts04` (functional wrappers)
- `dipole_tilt(t)`

See the [TsyganenkoModels.jl documentation](https://JuliaSpacePhysics.github.io/TsyganenkoModels.jl) for full details.
lasp-ase 0.0.4
ASE calculator for LASP

# lasp_ase
ASE calculator for LASP (http://www.lasphub.com)

## Installation

### 环境配置
* Linux 环境。没有测试过 windows 环境，只要 lasp 二进制支持就可以。
* 安装 intel mpi 编译器 （lasp 运行需要），参考 intel oneapi 官网。
* 从 LASP 官网下载 lasp 可执行文件和相关的势函数 （需要许可）。
* 在 .bashrc 中配置 `ASE_LASP_COMMAND = mpirun -np 4 lasp` （根据自己的情况修改）。若是使用 pbs 脚本则可以在脚本中加入相应配置。

### 安装

使用 `pip install -U lasp_ase`

## run example
```bash
cd example
bash run_ase.sh
```
uw-course 2.0.0
A course helper tool for managing UW courses schedule
Requires Python: >=3.10

# uw-course

[![PyPI version](https://badge.fury.io/py/uw-course.svg)](https://badge.fury.io/py/uw-course)


This is a project for UWaterloo students to help them get course information and generate a schedule for the next term.

MongoDB DashBoard: [LINK](https://charts.mongodb.com/charts-project-0-cbzai/public/dashboards/091bc68f-76df-48c0-aa69-b21af14c0a8a)

### Example Schedule: 

![Image text](demo.png)

---

### Install Steps: 
1. Install Python3 (>= 3.10) [Python Website](https://www.python.org/downloads/)
2. ```bash
   pip install uw-course
   ```

---

### Usage (TUI)

Run the terminal UI:
```bash
uw-course
```

You can:
- Check course details
- Build a schedule manually or load a plan file
- View the weekly schedule in the TUI
- Export the `.out` schedule to PDF

#### Schedule Plan File Format
1. Create a new file named `schema.txt`
2. Edit `schema.txt` with the following format:
```txt
Class{year}{Winter/Spring/Fall}

{Course}{CourseName}, {ClassID}
{Course}{CourseName}, {ClassID}
...
```

Example:
```txt
Class2025Winter

PHYS 234, 7166
CS 431, 8788
PMATH 351, 6382
CO 353, 6157
STAT 231, 6097
AMATH 250, 5967
```
3. Use the TUI to select “Load Plan” and provide the file path (default: `uw-course-files/schema.txt`).
4. The program will generate `uw-course-files/schedule.out`, and you can export it to PDF from the TUI.

#### Output Location
All generated files are stored in `uw-course-files/`:
- `uw-course-files/schedule.out`
- `uw-course-files/schedule.pdf`
- `uw-course-files/schema.txt` (if saved from the TUI)

#### Any Idea or Question, welcome send me an email via: j7zang@uwaterloo.ca

---

## Credits

PDF export uses a modified version of `pdfschedule` by John Thorvald Wodder II. Thanks for the original project.

---

## License

This project is open-source and can be modified and used for personal or educational purposes. Attribution to the original creator is appreciated. (MIT License)