semvec/pypi-packages huggingface dataset:
mcp-coze-server 0.1.12
A MCP server implementation for Coze API
Requires Python: >=3.11
Keywords: mcp, coze, api, server

# MCP Coze Server

A MCP server implementation for Coze API

## Installation

1. Install the package from PyPI:
```bash
pip install mcp-coze-server
```

2. Install additional dependencies:
```bash
pip install cozepy @ git+https://github.com/shizeying/coze-py.git@v0.13.1.post1
```

Or install from source:
```bash
git clone https://github.com/shizeying/coze-mcp-server.git
cd coze-mcp-server
pip install -r requirements.txt
pip install -e .
```

## Usage

1. 设置环境变量：

```bash
export COZE_API_TOKEN="your_api_token"
export COZE_API_BASE="https://api.coze.cn"  # 或其他API地址
```

2. 运行服务器：

```bash
python -m mcp_coze_server
```

## Configuration

Support the following configuration methods:

1. Environment variables:
   - COZE_API_TOKEN
   - COZE_API_BASE

2. Command line arguments:
   - --coze-api-token
   - --coze-api-base

## Development

1. Clone the repository:

```bash
git clone https://github.com/shizeying/coze-mcp-server.git
```

2. Install dependencies:

```bash
pip install -r requirements.txt
pip install -e .
```

## License

This project is licensed under the MIT License - see the LICENSE file for details.
msecrets 0.1.0
AWS Secret Manager
Requires Python: >=3.12

# MSecrets

Usage:
```
Usage: msecrets [OPTIONS] COMMAND [ARGS]...

Options:
  --help  Show this message and exit.

Commands:
  create  Create secret
  edit    Edit secret online
  env     Expose secret as enviromental variables
  get     Get specific value from the password
  list    List secrets
  set     Set specific value from the password
  view    View secret
```

For now just:
```
❯ pipx install git+ssh://git@github.com/hasiotis/msecrets.git
```
kisters.network_store.model_library 0.6.4
Model Library Base for the Kisters Network Store
Requires Python: >=3.9

# kisters.network_store.model_library

Base models and associated utilities for the KISTERS Network Store.

## A Note on Entrypoints

To publish a model library using entrypoints, add the domain and path to the
root directory of your library to the entrypoint
`kisters.network_store.model_library.util`. For example, the model library for
the water domain looks as follows:

```toml
[project.entry-points]
[project.entry-points."kisters.network_store.model_library.util"]
water = "kisters.network_store.model_library.water"
```

For the entrypoint to correctly identify the models in the library, the root
`__init__.py` must include the `links` and `nodes` modules. Furthermore, the
parent node of your library's nodes is expected to be called `_Node` and the
parent link is expected to be called `_Link`. All links and nodes in your
library will inherit from these two elements.
immutable-Python-Type 1.2.3
A immutable type for Python
Requires Python: >=3.9

# Immutable-Python-type
_package for make immutable all types in Python_

### Support types and replaced by:

- int = ``Int_()``
- float = ``Float_()``
- str = ``Str_()``
- bool = ``Bool_()``
- set = ``Set_()``
- tuple = ``Tuple_()``
- list = ``List_()``
- dict = ``Dict_()``

### Examples

```python
from immutableType import Bool_, Int_

immutable_bool = Bool_(True) # init immutable boolean
immutable_int = Int_(1) # init immutable integer

immutable_bool.bool_ = 1234 # raise an error
immutable_bool.bool_ = False # good

print(immutable_int == immutable_bool) # print False
"""
Immutable boolean consider True = 1 and False = 0
"""
```

### ``Int_(integer: int)``
```python
from immutableType import Int_

my_int = Int_(1234) # init immutable integer
my_int.int_ # property to get the value in 'int' type
```
> ``Int_()`` have methods :
> - ``__str__`` : return string with the integer value
> - ``__int__`` : return the integer value
> - ``__bool__`` : return **True** if the **value == 1** else **False**
> - ``__eq__, __and__, __or__, __iand, __ior__`` : basic comparaison
> - ``__add__, __sub__, __iadd__, __isub__, __mul__, __imul__, __truediv__, __itruediv__, __pow__, __ipow__`` : basic operation _(only int value are accepted for operation. The result of a division is always an integer. Use Float\_ class for more precision.)_

_redaction in progress..._
pnne-search 0.1.2
A package for search model estimation
Requires Python: >=3.6

This package `pnne_search` implements the pre-trained neural network estimator for sequential search model, as described by "Pre-Training Estimators for Structural Models: Application to Consumer Search"

See below for a simple demonstration of how to use `pnne_search`:

import time 
import pnne_search

data = pnne_search.load_example_data()

pnne_search.pnne_estimate(data['Y'], data['Xp'], data['Xa'], data['Xc'], 
                          data['consumer_idx'], checks = True)

start_time = time.time()
pnne_search.pnne_estimate(data['Y'], data['Xp'], data['Xa'], data['Xc'], 
                          data['consumer_idx'], checks = True, se=True)
time.time() - start_time

start_time = time.time()
pnne_search.pnne_estimate(data['Y'], data['Xp'], data['Xa'], data['Xc'], 
                          data['consumer_idx'], checks = True, se=True, use_parallel=False)
time.time() - start_time
xingshu-ctastrategy 1.0.1
专为中文母语开发者打造的量化交易研发框架。
Requires Python: >=3.8

# 星枢(xingshu)框架的CTA策略模块

## 说明
针对单标的CTA类量化策略设计的应用模块，用于实现CTA策略从代码开发、历史回测、参数优化到自动交易的全流程业务功能。

## 安装
### 直接使用pip命令：
pip install xingshu_ctastrategy

### 或者下载源代码后，解压后在cmd中运行：
pip install .
caravagene 0.1.9
Plot multi-part genetic constructs.
Keywords: SBOL, DNA, assembly, plot

Caravagene
==========

Caravagene (full documentation `here <https://edinburgh-genome-foundry.github.io/Caravagene/>`_)
is a Python library to plot schemas of DNA constructs from a list of parts.

Infos
-----

Caravagene requires `WkHTMLtoPDF <https://wkhtmltopdf.org/>`_ to be installed. On Ubuntu, install it with:

.. code:: shell

    apt-get install wkhtmltopdf

**PIP installation:**

.. code:: bash

  pip install caravagene

**Web documentation:** `<https://edinburgh-genome-foundry.github.io/Caravagene/>`_

**Github page:** `<https://github.com/Edinburgh-Genome-Foundry/Caravagene>`_

**Live demo:** `<https://cuba.genomefoundry.org/sketch_constructs>`_

**License:** MIT

Copyright 2017 Edinburgh Genome Foundry, University of Edinburgh
terminalfun-ready-20 1.0
20 gerçek terminal oyununu birleştiren Python paketi
Requires Python: >=3.6

# terminalfun_ready_20

Terminalde Ã§alÄ±ÅŸan 20 gerÃ§ek oyunu birleÅŸtiren Python paketi.

## KullanÄ±m

```python
from terminalfun_ready_20 import play
play("tetris")
```

veya terminalden:

```bash
terminalfun dino
```

## Oyunlar:
- snake
- pong
- tictactoe
- 2048
- dino
- tetris
- flappy
- minesweeper
- hangman
- rps
- geometry
- planetx
- numbercrush
- waspinvasion
- duckhunt
- pipes
- space_shooter
- arkanoid
- ascii_invaders
- ascii_doom