semvec/pypi-packages huggingface dataset:
igbyte 0.1.4
A lightweight Python package providing handy Instagram utilities including search profile by user id,sending reset on acc,downloading reels
Requires Python: >=3.7
Keywords: instagram api

# igbyte 🚀

**igbyte** is a lightweight Python package that provides practical Instagram utilities, including:

- Fetching public Instagram user information via user ID / username
- Extracting reel metadata (caption, hashtags, video link)  
- Resetting Instagram accounts using Web or Android APIs  
- Downloading Instagram reels effortlessly  
- initiate Instagram webSignup request

---

## 📦 Installation

Install using pip:

```bash
pip install igbyte
```

---

## 🛠 Features & Usage

### 1️⃣ Fetch Instagram Profile Info

```python
from igbyte import iguid_info

# Replace with a real user ID
user_info = iguid_info("3954561043")
print(user_info)
```

---

### 2️⃣ Reset Instagram Account

#### Available Reset Methods:
1. **Web API → `igresetv1`**  
2. **Android API → `igresetv2`**

```python
from igbyte import igresetv1, igresetv2

# Web API reset
reset_web = igresetv1("ankucode")
print(reset_web)

# Android API reset
reset_android = igresetv2("ankucode")
print(reset_android)
```
---

### -- Fetch Instagram Profile Info by Username
```python
from igbyte import infoig

# Replace with a real username
username_info = infoig("ankucode")
print(username_info)
```
---

### 3️⃣ Download Instagram Reels

```python
from igbyte import download_reel

reel_data = download_reel("https://www.instagram.com/reel/")
print(reel_data)
```

---

### -- Generate Instagram Cookies

```python
from igbyte import gen_igcookie

print(gen_igcookie)
```
---
## --Initiate web Signup Request
```python
from igbyte import initiate_signup

response = initiate_signup(
    username="your_username",
    email="your_email@example.com"
)

print(response)

```

## 📧 Contact & Author

**Author:** AnkuCode

**Email:** ankucode@gmail.com

**Telegram:** [@ankucode](https://t.me/ankucode)

**GITHUB:** [@ankuucode](https://github.com/ankuucode)

**Get Module Update:** [@IGByte](https://t.me/IgByte)
iflow-mcp_mcp-python-demo 0.1.0
MCP Python Demo with Web UI
Requires Python: >=3.10

# MCP Python Demo

这是一个基于 MCP (Model Context Protocol) 的项目，集成了智谱 AI 和腾讯地图 API，提供了天气查询、地理编码和网络搜索等功能。项目包含命令行界面和 Web 界面两种交互方式。

## 功能特性

- 🤖 智谱 AI 驱动的对话系统
- 🌤️ 腾讯地图天气查询
- 🗺️ 地理编码查询
- 🔍 智能网络搜索
- 💻 命令行和 Web 界面双重支持
- 🌐 支持外网访问

## 项目结构

```
mcp-python-demo/
├── src/
│   └── mcp_python_demo/
│       ├── __init__.py
│       ├── server.py
│       ├── client.py
│       ├── web_client.py
│       └── main.py
├── images/
├── pyproject.toml
├── .env
├── .env.example
├── .gitignore
└── README.md
```

## 快速开始

### 环境要求

- Python >= 3.10
- uv (推荐) 或 pip

### 安装

1. 克隆项目：
```bash
git clone https://github.com/chenchen0611/mcp-python-demo.git
cd mcp-python-demo
```

2. 使用 uv 安装依赖：
```bash
uv venv
source .venv/bin/activate  # Linux/Mac
# 或
.venv\Scripts\activate  # Windows
uv pip install -e .
```

### 配置

1. 创建 `.env` 文件并配置必要的环境变量：
```bash
# 智谱AI配置
ZHIPU_API_KEY="your-zhipu-api-key"
ZHIPU_MODEL="glm-4-plus"
ZHIPU_BASE_URL="https://open.bigmodel.cn/api/paas/v4/"

# 腾讯地图API配置
TENCENT_MAP_API_KEY="your-tencent-map-api-key"
TENCENT_MAP_API_BASE="https://apis.map.qq.com/ws/"
```

### 运行

#### Web 界面（推荐）

```bash
cd src/mcp_python_demo
streamlit run web_client.py
```

访问以下地址之一：
- 本地访问：http://localhost:8501
- 局域网访问：http://[your-local-ip]:8501
- 外网访问：http://[your-public-ip]:8501

##### 页面配置

 - 直接输入server.py,点击连接
 ![config](images/stdio.png)
 - 新建一个terminal，运行下方命令然后在web页面输入http://127.0.0.1:8000
    ```bash
    cd src/mcp_python_demo
    uv run server.py
    ```
    ![config](images/sse.png)

#### 命令行界面

```bash
cd src/mcp_python_demo
# 使用本地服务器
uv run client.py --agent server.py

# 或使用远程服务器
uv run server.py
uv run client.py --agent http://127.0.0.1:8000
```
![config](images/terminal.png)

## 可用工具

### 1. 天气查询
```python
async def query_weather(adcode: str, search_type: str = "now") -> str:
    """获取中国城市天气信息"""
```

### 2. 地理编码查询
```python
async def query_adcode(region_name: str) -> str:
    """获取行政区划代码"""
```

### 3. 网络搜索
```python
async def web_search(search_query: str, search_engine: str = "search_std") -> str:
    """使用智谱AI进行网络搜索"""
```

## 开发说明

### 添加新工具

1. 在 `server.py` 中使用 `@mcp.tool()` 装饰器添加新工具：
```python
@mcp.tool()
async def your_tool(param1: str, param2: str = "default") -> str:
    """工具描述"""
    # 实现逻辑
    return result
```

2. 工具会自动在客户端可用

### 环境变量

- 所有敏感信息和配置都应该放在 `.env` 文件中
- 不要将 `.env` 文件提交到版本控制系统
- 参考 `.env.example` 进行配置

## 常见问题

1. **连接错误**
   - 检查服务器地址是否正确
   - 确认所有环境变量都已正确配置
   - 确保服务器正在运行且端口可访问

2. **API 调用失败**
   - 验证 API 密钥是否有效
   - 检查网络连接
   - 确认 API 调用频率是否超限

3. **Web UI 无法访问**
   - 检查防火墙设置
   - 确认端口是否开放
   - 验证服务器 IP 地址是否正确

## 致谢

本项目基于以下开源项目：

- [Model Context Protocol Servers](https://github.com/modelcontextprotocol/servers) - MCP 官方服务器参考实现，提供了丰富的示例和最佳实践
- [MCP Python SDK](https://github.com/modelcontextprotocol/python-sdk) - Python SDK 实现，提供了简洁优雅的 API 接口
iflow-mcp_mcp_server_trino 0.1.2
A Model Context Protocol (MCP) server that enables secure interaction with Trino. This server allows AI assistants to list tables, read data, and execute SQL queries through a controlled interface, making data exploration and analysis safer and more structured.
Requires Python: >=3.11

# Trino MCP Server

This repository provides an MCP (Model-Control-Protocol) server that allows you to list and query tables via Trino using Python.

## Overview

- MCP: MCP is a protocol for bridging AI models, data, and tools. This example MCP server provides:
    - A list of Trino tables as MCP resources
    - Ability to read table contents through MCP
    - A tool for executing arbitrary SQL queries against Trino
- Trino: A fast, distributed SQL query engine for big data analytics. This server makes use of Trino’s Python client (trino.dbapi) to connect to a Trino host, catalog, and schema.

## Requirements

- Python 3.9+ (or a version compatible with mcp, trino, and asyncio)
- trino (the Python driver for Trino)
- mcp (the Model-Control-Protocol Python library)

## Configuration

The server reads Trino connection details from environment variables:

| Variable         | Description                                                          | Default     |
|------------------|----------------------------------------------------------------------|------------|
| `TRINO_HOST`     | Trino server hostname or IP                                          | `localhost`|
| `TRINO_PORT`     | Trino server port                                                    | `8080`     |
| `TRINO_USER`     | Trino user name                                                      | *required* |
| `TRINO_PASSWORD` | Trino password (optional, depends on your authentication setup)      | (empty)    |
| `TRINO_CATALOG`  | Default catalog to use (e.g., `hive`, `tpch`, `postgresql`, etc.)    | *required* |
| `TRINO_SCHEMA`   | Default schema to use (e.g., `default`, `public`, etc.)             | *required* |

## Usage

``` json
{
  "mcpServers": {
    "trino": {
      "command": "uv",
      "args": [
        "--directory", 
        "<path_to_mcp_server_trino>",
        "run",
        "mcp_server_trino"
      ],
      "env": {
        "TRINO_HOST": "<host>",
        "TRINO_PORT": "<port>",
        "TRINO_USER": "<user>",
        "TRINO_PASSWORD": "<password>",
        "TRINO_CATALOG": "<catalog>",
        "TRINO_SCHEMA": "<schema>"
      }
    }
  }
}

```
tensordiagram-mcp 0.1.0
MCP server for drawing tensors with tensordiagram
Requires Python: >=3.9

# tensordiagram mcp server

mcp server that enables AI assistants like Claude to draw diagrams of tensors.

## overview

This mcp server provides the `draw_tensor` tool, allowing LLMs to create clear, visual representations of tensors to help users understand tensor shapes, operations, and dimensional transformations. Built on top of [tensordiagram](https://github.com/hardik-vala/tensordiagram) and [FastMCP](https://github.com/jlowin/fastmcp).

## install

### option 1: PyPI

```bash
pip install tensordiagram-mcp
```

### option 2: from source

```bash
git clone https://github.com/hardik-vala/tensordiagram.git
cd tensordiagram/mcp
pip install -e .
```

### option 3: Docker

```bash
git clone https://github.com/hardik-vala/tensordiagram.git
cd tensordiagram/mcp
docker build -t tensordiagram/mcp:latest .
docker run -i --rm --name tensordiagram-mcp tensordiagram/mcp:latest
```

## configuration

### Claude Desktop

Add this to your Claude Desktop configuration file:

**macOS**: `~/Library/Application Support/Claude/claude_desktop_config.json`
**Windows**: `%APPDATA%/Claude/claude_desktop_config.json`

#### using pip installation

```json
{
  "mcpServers": {
    "tensordiagram": {
      "command": "python",
      "args": ["-m", "tensordiagram_mcp"]
    }
  }
}
```

#### using local installation

```json
{
  "mcpServers": {
    "tensordiagram": {
      "command": "python",
      "args": ["/path/to/tensordiagram/mcp/server.py"]
    }
  }
}
```

#### using Docker

```json
{
  "mcpServers": {
    "tensordiagram": {
      "command": "docker",
      "args": ["run", "-i", "--rm", "--name", "tensordiagram-mcp", "tensordiagram/mcp:latest"]
    }
  }
}
```

After updating the configuration, restart Claude Desktop.

## usage

Once installed, Claude can use the `draw_tensor` tool to create visualizations. Here are some examples:

### example prompts

> "Can you show me what a 3x4 tensor looks like?"
> "Draw a 2x3x4 tensor in coral color"
> "Visualize a 3x3 identity matrix with the actual values shown"
> "Create a diagram of a 4x6 tensor with dimension sizes and indices labeled"

## technical details

- **minimum width**: all output images are scaled to at least 400px width for readability
- **size limit**: images are capped at ~900KB to comply with MCP's 1MB message limit
- **image format**: PNG with white background
- **dependencies**: requires pycairo for PNG rendering

## license

MIT License - see the main tensordiagram repository for details.
iflow-mcp_jewish_library 0.1.0
the jewish library, accessible to LLMs 
Requires Python: >=3.10

# Jewish Library MCP Server

An MCP (Model Context Protocol) server that provides powerful search capabilities for Jewish texts and literature. This server enables Large Language Models to search and reference Jewish texts through a standardized interface.

## Features

- Full-text search across Jewish texts and literature
- Advanced query syntax support:
  - Field-specific search (text:term, reference:term, topics:term)
  - Boolean operators (AND, OR)
  - Required/excluded terms (+term, -term)
  - Phrase search ('exact phrase')
  - Wildcards (?, *)
- Relevance-based scoring
- Rich search results including references, topics, and highlighted excerpts

## Installation

Requires Python 3.10 or higher.


### Clone the repository
```bash
git clone https://github.com/sivan22/mcp-otzaria-server.git
cd mcp-otzaria-server
```
### Get the index
download and extract the index from [here](https://drive.google.com/file/d/1lpbBCPimwcNfC0VZOlQueA4SHNGIp5_t/view?usp=drive_link)

### Install dependencies
```
pip install .
```
## Running the Server

The server can be run directly:

```bash
uv --directory path/to/directory run jewish_library
```

Or through an MCP client that supports the Model Context Protocol.
for claude desktop app and cline you should use the following config:
```
{
  "mcpServers": {        
      "jewish_library": {
          "command": "uv",
          "args": [
              "--directory",
              "your/path/to/directory",
              "run",
              "jewish_library"
          ],
          "env": {
            "PYTHONIOENCODING": "utf-8" 
          }
      }
  }
}
```

## Available tools

The server provides a single tool through the MCP interface:

### full_text_search

Performs a full-text search across the Jewish library with advanced query capabilities.

Example query formats:
```
# Basic search
"maimonides on prayer"

# Field-specific search
text:"love your neighbor" AND topics:mitzvot

# Required terms
+shabbat +candles

# Phrase search with topic filter
"four species" AND topics:sukkot

# Wildcard search
pray* AND reference:psalms
```

Search results include:
- Reference information
- Relevant topics
- Highlighted excerpts showing query matches
- Relevance score

## Development

This project uses:
- [MCP SDK](https://github.com/modelcontextprotocol/sdk) for server implementation
- [Tantivy](https://github.com/quickwit-oss/tantivy) for full-text search capabilities





## Requirements

- Python >= 3.10
- MCP SDK >= 1.1.1
- Tantivy search engine

## License

MIT License
mokeidb 0.1.8
Create MongoDB models like creating a dataclass
Requires Python: >=3.10

# MokeiDB

MokeiDB is an ORM model for MongoDB.

Write MongoDB models like you would write a dataclass:

    @database('MyDatabase')
    class Company(MongoDbModel):
        registration_number: int
        name: str
        incorporated: datetime.datetime

You can create complex models by including one model in another:

    @database('MyDatabase')
    class Employee(MongoDbModel):
        employee_id: int
        company: Company

To retrieve an object from the database:

    company = Company.find_one(name='Python Inc.')

To get all objects matching a particular query:

    employees = Employee.find(company=company)

To make a change to the company:

    company.name = 'Pythonistas Inc.'
    company.save()  # writes to database

Or update and write fields in one line:

    company.update(name='Snakes Inc.')