semvec/pypi-packages huggingface dataset:
evotoolkit 1.0.0rc6
LLM-driven code evolution method library.
License: MIT
Requires Python: >=3.10
Keywords: LLM, evolutionary, optimization, toolkit

# EvoToolkit

[![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/pgg3/evotoolkit)




**LLM-driven solution evolutionary optimization toolkit** 

EvoToolkit is a Python library that leverages Large Language Models (LLMs) to evolve solutions for optimization problems. It combines the power of evolutionary algorithms with LLM-based solution generation and refinement.

## Installation

```bash
pip install evotoolkit
```

## Quick Start

```python
import evotoolkit
from evotoolkit.task.python_task.scientific_regression import ScientificRegressionTask
from evotoolkit.task.python_task import EvoEngineerPythonInterface
from evotoolkit.tools import HttpsApi

# 1. Create a task
task = ScientificRegressionTask(dataset_name="bactgrow")

# 2. Create an interface
interface = EvoEngineerPythonInterface(task)

# 3. Solve with LLM
llm_api = HttpsApi(
    api_url="https://api.openai.com/v1/chat/completions",
    key="your-api-key-here",
    model="gpt-4o"
)
result = evotoolkit.solve(
    interface=interface,
    output_path='./results',
    running_llm=llm_api,
    max_generations=5
)
```

## Features

- 🤖 **LLM-Driven Evolution**: Use language models to generate and evolve solutions
- 🔬 **Multiple Algorithms**: EoH, EvoEngineer, and FunSearch
- 🌍 **Task-Agnostic**: Supports code, text, math expressions, etc.
- 🎯 **Extensible**: Easy-to-extend task system
- 🔌 **Simple API**: High-level `evotoolkit.solve()` function

## Documentation

Full documentation: https://evotoolkit.readthedocs.io/

## Citation

If you use EvoToolkit in your research, please cite:

```bibtex
@article{guo2025evotoolkit,
  title={evotoolkit: A Unified LLM-Driven Evolutionary Framework for Generalized Solution Search},
  author={Guo, Ping and Zhang, Qingfu},
  journal={arXiv preprint arXiv:XXXX.XXXXX},
  year={2025},
  note={Submitted to arXiv}
}
```

## License

MIT License. For academic use, please cite our paper above.
mcp-cuc-tangxr 0.2.0
An MCP Server that provides a tool to get the current time with optional timezone support
License: MIT
Requires Python: <4.0,>=3.10

# MCP Time Server

An MCP Server that provides a tool to get the current time with optional timezone support.

## Features

- Provides a `get_current_time` tool to retrieve the current time
- Supports optional timezone parameter
- Simple and lightweight implementation
- Built using the Model Context Protocol (MCP)

## Installation

```bash
pip install mcp-time-server
```

## Usage

### Running the Server

```bash
mcp-time-server
```

This will start the MCP Server using the STDIO transport, which is suitable for local process communication.

### Using the `get_current_time` Tool

The server exposes a single tool called `get_current_time` that can be used by MCP clients.

#### Parameters

- `timezone` (optional): A timezone string, e.g., "Asia/Shanghai", "America/New_York"
  - If not provided, the system's default timezone will be used

#### Return Value

A formatted string representing the current time in the format: `YYYY-MM-DD HH:MM:SS.SSSSSS TIMEZONE`

#### Examples

```python
# Get current time in system default timezone
result = await mcp_client.call_tool("get_current_time")
# Example output: "2023-12-25 13:45:30.123456 EST"

# Get current time in a specific timezone
result = await mcp_client.call_tool("get_current_time", timezone="Asia/Shanghai")
# Example output: "2023-12-26 02:45:30.123456 CST"
```

## Project Structure

```
mcp-time-server/
├── mcp_time_server/
│   ├── __init__.py      # Package initialization
│   └── main.py          # Main server implementation
├── pyproject.toml       # Project configuration
├── README.md            # This file
├── LICENSE              # License file
└── .gitignore           # Git ignore file
```

## License

MIT License - see the LICENSE file for details.

## About MCP

The Model Context Protocol (MCP) is an open-source standard for connecting AI applications to external systems. For more information, visit [modelcontextprotocol.io](https://modelcontextprotocol.io).