semvec/pypi-packages huggingface dataset:
kiwoom-rest-api 0.1.12
Kiwoom REST API client for Python
Requires Python: <4.0,>=3.9

# Kiwoom REST API
Python client for interacting with [Kiwoom REST API](https://openapi.kiwoom.com/).


## Table of Contents
- [Introduction](#introduction)
- [Features](#features)
- [Installation](#installation)
  - [Using pip](#using-pip)
  - [Using uv](#using-uv)
  - [Using Poetry](#using-poetry)
- [Usage](#Usage)
- [CLI Usage](#CLI-Usage)
  - [Using uvx](#Using-uvx)
  - [Set API Key](#Set-API-Key)
- [Docs](#Docs)
- [License](#license)

## Installation

### using pip
```bash
pip install kiwoom-rest-api
```

### using uv
```
uv add kiwoom-rest-api
```

### using poetry
```
poetry add kiwoom-rest-api
```

## Usage

```python
    import os
    os.environ["KIWOOM_API_KEY"] = "your_api_key"
    os.environ["KIWOOM_API_SECRET"] = "your_api_secret"

    from kiwoom_rest_api.koreanstock.stockinfo import StockInfo
    from kiwoom_rest_api.auth.token import TokenManager

    # 토큰 매니저 초기화
    token_manager = TokenManager()

    # StockInfo 인스턴스 생성 (base_url 수정)
    stock_info = StockInfo(base_url="https://api.kiwoom.com", token_manager=token_manager)

    try:
        result = stock_info.basic_stock_information_request_ka10001("005930")
        print("API 응답:", result)
    except Exception as e:
        print("에러 발생:", str(e))
```

## CLI Usage

### Using uvx
```bash
    uvx --from kiwoom-rest-api kiwoom -k "YOUR_KEY" -s "YOUR_SECRET" ka10001 005930
```

### Set API Key
```bash
    # Linux/macOS/Windows(git bash)
    export KIWOOM_API_KEY="YOUR_ACTUAL_API_KEY"
    export KIWOOM_API_SECRET="YOUR_ACTUAL_API_SECRET"

    # Windows (CMD)
    set KIWOOM_API_KEY="YOUR_ACTUAL_API_KEY"
    set KIWOOM_API_SECRET="YOUR_ACTUAL_API_SECRET"

    # Windows (PowerShell)
    $env:KIWOOM_API_KEY="YOUR_ACTUAL_API_KEY"
    $env:KIWOOM_API_SECRET="YOUR_ACTUAL_API_SECRET"
```

```bash
    # 가상 환경 활성화 (필요시)
    poetry shell

    # 도움말 보기
    kiwoom --help
    kiwoom ka10001 --help

    # ka10001 명령어 실행 (환경 변수 사용 시)
    kiwoom ka10001 005930 # 삼성전자 예시

    # ka10001 명령어 실행 (옵션 사용 시)
    kiwoom --api-key "YOUR_KEY" --api-secret "YOUR_SECRET" ka10001 005930

    # 다른 base URL 사용 시
    kiwoom --base-url "https://mockapi.kiwoom.com" ka10001 005930
```

# Docs
[pypi](https://pypi.org/project/kiwoom-rest-api/)
[github](https://github.com/bamjun/kiwoom-rest-api)

# License

This project is licensed under the terms of the MIT license.
infercnv-mcp 0.2.0
Natural language interface for CNV inference from scRNA-Seq data with infercnvpy through MCP.
Requires Python: >=3.10
Keywords: AI, CNV, agent, bioinformatics, llm, mcp, model context protocol, scRNA-seq, single cell

# Infercnv-MCP

Natural language interface for Copy Number Variation (CNV) inference from scRNA-Seq data with infercnvpy through MCP.

## 🪩 What can it do?

- IO module for reading and writing scRNA-Seq data, load gene position 
- Preprocessing module for neighbors computation and data preparation
- Tool module for CNV inference, cnv score
- Plotting module for chromosome heatmaps, UMAP, and t-SNE visualizations

## ❓ Who is this for?

- Researchers who want to infer CNVs from scRNA-Seq data using natural language
- Agent developers who want to integrate CNV analysis into their applications

## 🌐 Where to use it?

You can use infercnv-mcp in most AI clients, plugins, or agent frameworks that support the MCP:

- AI clients, like Cherry Studio
- Plugins, like Cline
- Agent frameworks, like Agno 

## 📚 Documentation

scmcphub's complete documentation is available at https://docs.scmcphub.org

## 🏎️ Quickstart

### Install

Install from PyPI
```
pip install infercnv-mcp
```
you can test it by running
```
infercnv-mcp run
```

#### run infercnv-mcp locally
Refer to the following configuration in your MCP client:

check path
```
$ which infercnv 
/home/test/bin/infercnv-mcp
```

```
"mcpServers": {
  "infercnv-mcp": {
    "command": "/home/test/bin/infercnv-mcp",
    "args": [
      "run"
    ]
  }
}
```

#### Run infercnv-server remotely
Refer to the following configuration in your MCP client:

Run it in your server
```
infercnv-mcp run --transport shttp --port 8000
```

Then configure your MCP client, like this:
```
http://localhost:8000/mcp
```

## 🤝 Contributing

If you have any questions, welcome to submit an issue, or contact me(hsh-me@outlook.com). Contributions to the code are also welcome!

## Citing
If you use infercnv-mcp in your research, please consider citing following work: 
> https://github.com/icbi-lab/infercnvpy
decoupler-mcp 0.4.0
Natural language interface for biological activities analysis with decoupler through MCP.
Requires Python: >=3.10
Keywords: AI, agent, bioinformatics, llm, mcp, model context protocol, scRNA-seq, single cell

# decoupler-MCP

Natural language interface for scRNA-Seq analysis with decoupler through MCP.

## 🪩 What can it do?

- IO module like read and write scRNA-Seq data
- Pathway activity/Transcription factor  inference
- Tool module, like clustering, differential expression etc.
- Plotting module, like violin, umap/tsne

## ❓ Who is this for?

- Anyone who wants to do scRNA-Seq analysis natural language!
- Agent developers who want to call decoupler's functions for their applications

## 🌐 Where to use it?

You can use decoupler-mcp in most AI clients, plugins, or agent frameworks that support the MCP:

- AI clients, like Cherry Studio
- Plugins, like Cline
- Agent frameworks, like Agno 

## 🎬 Demo

A demo showing scRNA-Seq cell cluster analysis in a AI client Cherry Studio using natural language based on decoupler-mcp


## 📚 Documentation

scmcphub's complete documentation is available at https://docs.scmcphub.org


## 🏎️ Quickstart

### Install

Install from PyPI
```
pip install decoupler-mcp
```
you can test it by running
```
decoupler-mcp run
```



#### run decoupler-mcp locally
Refer to the following configuration in your MCP client:

check path
```
$ which decoupler 
/home/test/bin/decoupler-mcp
```

```
"mcpServers": {
  "decoupler-mcp": {
    "command": "/home/test/bin/decoupler-mcp",
    "args": [
      "run"
    ]
  }
}
```

#### run decoupler-server remotely
Refer to the following configuration in your MCP client:

run it in your server
```
decoupler-mcp run --transport shttp --port 8000
```

Then configure your MCP client in local AI client, like this:
```

"mcpServers": {
  "decoupler-mcp": {
    "url": "http://localhost:8000/mcp"
  }
}
```

## 🤝 Contributing

If you have any questions, welcome to submit an issue, or contact me(hsh-me@outlook.com). Contributions to the code are also welcome!

## Citing

If you use decoupler-mcp in for your research, please consider citing  following work: 
> Badia-i-Mompel P., Vélez Santiago J., Braunger J., Geiss C., Dimitrov D., Müller-Dott S., Taus P., Dugourd A., Holland C.H., Ramirez Flores R.O. and Saez-Rodriguez J. 2022. decoupleR: ensemble of computational methods to infer biological activities from omics data. Bioinformatics Advances. https://doi.org/10.1093/bioadv/vbac016