semvec/pypi-packages huggingface dataset:
regexplayground 2.1.0
Playground for Python Regular Expressions. Also includes cheatsheet and interactive examples.
Requires Python: >=3.9

# Python re(gex)? playground

This TUI application is intended as an interactive playground for Python Regular Expressions. The app also includes a comprehensive cheatsheet and several interactive examples.

# Screenshot

![Sample screenshot from the Playground screen](https://github.com/learnbyexample/TUI-apps/raw/main/PyRegexPlayground/pyregex_finditer.png)

![Sample screenshot from the Interactive Examples screen](https://github.com/learnbyexample/TUI-apps/raw/main/PyRegexPlayground/pyregex_examples.png)

# Guide

See [app_guide.md](https://github.com/learnbyexample/TUI-apps/blob/main/PyRegexPlayground/app_guide.md)

# License

Code snippets are licensed under [MIT LICENSE](https://github.com/learnbyexample/TUI-apps/blob/main/LICENSE)

Explanations and associated files (like `cheatsheet.md`) are licensed under [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License](https://creativecommons.org/licenses/by-nc-sa/4.0/)
python-cptv 0.0.9
This is Python package provides for quick parsing Cacophony Project Thermal Video (CPTV) files. 
Requires Python: >=3.8

# Python bindings

## Running the example

To build the module run `./build.sh`, which will put a native module called `ctpv_rs_python_bindings.so` into
the `./examples` folder.
Then in a python virtual environment with `numpy` installed, you can run `python python-cptv-decoder.py` from inside
the `examples` folder.

## Release to pypi

Update `pyproject.toml` version identifier then create a release on github this will upload a new version to https://pypi.org/project/python-cptv
iflow-mcp_i-akiya_shiranui 0.1.0
CDISC Library Retriever: A tool to retrieve the metadata from CDISC Library.
Requires Python: >=3.11

# Shiranui
Shiranui is [the Model Context Protocol](https://modelcontextprotocol.io/introduction)(MCP) Server for retreiving the clinical standard contents in [the CDISC Library](https://library.cdisc.org/browser/#/).  
This MCP server is built with [Python FastMCP](https://github.com/jlowin/fastmcp).

https://github.com/user-attachments/assets/9cd7e1a6-2750-4910-bb03-763c323b9f22

## Support CDISC Library API
- v2 Biomedical Concept Endpoints
- v2 Dataset Specialization Endpoints
- Controlled Terminology Codelist Endpoints
- ADaM Variable Metadata Endpoints
- SDTM Metadata Endpoints
- CDASH Metadata Endpoints
- SEND Metadata Endpoints
- Search Tool

## Requirements
- [Python v3.13](https://www.python.org/downloads/) and [UV](https://docs.astral.sh/uv/getting-started/installation/) were installed on your device.
- You have the [CDISC Library API Key](https://api.developer.library.cdisc.org/signin?returnUrl=%2Fapi-details).
  
## Installation
Download Shiranui from the [Releases Page](https://github.com/i-akiya/Shiranui/releases).
### Mac and Linux
```bash
cd /your/shiranui/dir
uv sync
```
Set your API key as an environment variable named CDISC_LIBRARY_API_KEY.   
### Windows
Use back slash "\\" for path separator charactor.
```powershell
cd C:\your\shiranui\dir
uv sync
```
Set your API key as an environment variable named CDISC_LIBRARY_API_KEY.   
  
## Configure mcp client
### LM Studio
#### Mac and Linux
```
{
  "mcpServers": {
    "Shiranui": {
      "command": "uv",
      "args": [
        "run",
        "/full/path/to/shiranui/dir/.venv/bin/shiranui"
      ]
    }
  }
}
```
#### Windows
Use slash"/" for path separator charactor.
```
{
  "mcpServers": {
    "Shiranui": {
      "command": "uv",
      "args": [
        "run",
        "C:/full/path/to/shiranui/dir/.venv/Scripts/shiranui"
      ]
    }
  }
}
```
You need to replace "/full/path/to/shiranui/dir" to match your own environment.  
In addition to LM Studio, it may also work with Claude Desktop, Codename Goose, and other MCP Clients.

  
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
iflow-mcp_kyryl-opens-ml_mcp-server-dagster 0.1.2
Add your description here
Requires Python: >=3.12

# mcp-dagster: A Dagster MCP Server

> The [Model Context Protocol (MCP)](https://modelcontextprotocol.io/introduction) is an open protocol that enables seamless integration between LLM applications and external data sources and tools. This repository provides an MCP server for interacting with [Dagster](https://dagster.io/), the data orchestration platform.

## Overview

A Model Context Protocol server that enables AI agents to interact with Dagster instances, explore data pipelines, monitor runs, and manage assets. It serves as a bridge between LLMs and your data engineering workflows.

Read our [launch post](https://kyrylai.com/2025/04/09/dagster-llm-orchestration-mcp-server/) to learn more.

[![PyPI version](https://badge.fury.io/py/mcp-server-dagster.svg)](https://pypi.org/project/mcp-server-dagster/)
[![Tests](https://github.com/kyryl-opens-ml/mcp-server-dagster/actions/workflows/pypi-publish.yaml/badge.svg)](https://github.com/kyryl-opens-ml/mcp-server-dagster/actions/workflows/pypi-publish.yaml)


## Components

### Tools

The server implements several tools for Dagster interaction:

- `list_repositories`: Lists all available Dagster repositories
- `list_jobs`: Lists all jobs in a specific repository
- `list_assets`: Lists all assets in a specific repository
- `recent_runs`: Gets recent Dagster runs (default limit: 10)
- `get_run_info`: Gets detailed information about a specific run
- `launch_run`: Launches a Dagster job run
- `materialize_asset`: Materializes a specific Dagster asset
- `terminate_run`: Terminates an in-progress Dagster run
- `get_asset_info`: Gets detailed information about a specific asset

## Configuration

The server connects to Dagster using these defaults:
- GraphQL endpoint: `http://localhost:3000/graphql`
- Transport: SSE (Server-Sent Events)

## Quickstart

### Running the Example

1. Start the Dagster instance with your pipeline:
```bash
uv run dagster dev -f ./examples/open-ai-agent/pipeline.py
```

2. Run the MCP server with SSE transport:
```bash
uv run examples/open-ai-agent/run_sse_mcp.py
```

3. Start the agent loop to interact with Dagster:
```bash
uv run ./examples/open-ai-agent/agent.py
```

### Example Interactions

Once the agent is running, you can ask questions like:

- "What assets are available in my Dagster instance and what do they do?"
- "Can you materialize the continent_stats asset and show me the result?"
- "Check the status of recent runs and provide a summary of any failures"
- "Create a new monthly aggregation asset that depends on continent_stats"

The agent will use the MCP server to interact with your Dagster instance and provide answers based on your data pipelines.