semvec/pypi-packages huggingface dataset:
mcp-zendesk 0.0.2
MCP server for Zendesk integration
Requires Python: >=3.10

# Zendesk MCP Server

A Model Context Protocol (MCP) server for Zendesk integration. This server provides tools for interacting with Zendesk, including ticket management, user operations, and search functionality.

## Features

- **Ticket Management**: Create, read, update, and comment on support tickets
- **User Management**: View user information and details
- **Search**: Advanced ticket search with flexible query options
- **Resources**: Access Zendesk tickets, users, groups, and ticket fields
- **Prompts**: Templates for creating tickets, crafting responses, and analyzing data

## Installation

```bash
pip install mcp-zendesk
```

## Configuration

Set the following environment variables:

```bash
export ZENDESK_BASE_URL="https://your-subdomain.zendesk.com"
export ZENDESK_EMAIL="your_email@example.com"
export ZENDESK_API_TOKEN="your_api_token"
```

To get an API token, visit Admin > Channels > API in your Zendesk Support admin interface.

## Usage

### Starting the server directly

```bash
mcp-zendesk
```

### Using with Claude Desktop

Add the following to your `claude_desktop_config.json` file:

```json
"mcp-zendesk": {
  "command": "uvx",
  "args": [
    "mcp-zendesk"
  ],
  "env": {
    "ZENDESK_BASE_URL": "https://your-subdomain.zendesk.com",
    "ZENDESK_EMAIL": "your_email@example.com",
    "ZENDESK_API_TOKEN": "your_api_token"
  }
}
```

Replace the environment variables with your actual Zendesk credentials.

## Available Tools

* **get_ticket**: Get details of a specific Zendesk ticket
* **create_ticket**: Create a new ticket with subject, description, priority, and tags
* **update_ticket**: Update status, priority, or add comments to existing tickets
* **search_tickets**: Search tickets with custom queries and sorting options
* **get_user**: Get details of a specific Zendesk user
* **add_ticket_comment**: Add public or private comments to tickets

## Available Resources

* **zendesk://tickets**: List of all recent Zendesk tickets
* **zendesk://users**: List of all Zendesk users
* **zendesk://ticket-fields**: List of all ticket fields (default and custom)
* **zendesk://groups**: List of support groups in the Zendesk account

## Available Prompts

* **create_ticket**: Template for creating a new ticket with appropriate details
* **ticket_response**: Template for drafting professional responses to tickets
* **ticket_analysis**: Template for analyzing ticket data and trends

## Example Queries

Once connected to Claude, you can ask questions like:

- "Show me all open tickets with high priority"
- "Create a new ticket for a customer who can't log into their account"
- "Find all tickets submitted by user@example.com"
- "What's the status of ticket #12345?"
- "Add a reply to ticket #12345 explaining how to reset a password"

## Version

0.0.1
mutube 0.1.2
Open source 1of10/Viewstats alternative. Find out if a youtube video is a relative outlier.
Requires Python: >=3.13

# µ-tube

Open source 1of10/Viewstats alternative. Find out if a youtube video is a relative outlier.

<img width="1038" alt="Screenshot 2025-04-27 at 12 14 48 AM" src="https://github.com/user-attachments/assets/7892c143-6d2b-41dc-8e55-0e8082763264" />

## Background

I've worked with [LukeJ](https://www.youtube.com/lukejtv) for a while now and we are attempting to make the best videos we can. There are several popular youtube analytic tools out there ([Viewstats](https://www.viewstats.com) and [1of10](https://1of10.com) to name a couple) but these tools are often expensive and closed source. A lot of the functionality behind these popular tools really isn't that complicated so this is an attempt to bring some of this functionality to people who are trying to make good content and can't afford these alternatives. As I write this, mutube's functionality is pretty basic but the hope is that I'll expand its capabilities as I continue to make videos.

## 📦 Installation

#### Unix / MacOS:

```bash
# install uv (package manager):
curl -LsSf https://astral.sh/uv/install.sh | sh

# restart your terminal, or run the following command:
source $HOME/.local/bin/env # or follow instructions

# install mutube through uv
uv tool install --python 3.13 mutube
```

## 🚀 Usage

```bash
# add api key found from google cloud for YouTube v3 (will add instructions on this later)
mutube key {YOUTUBE_API_KEY}
# currently the only supported feature is finding out if some input URL is a relative outlier compared to the 9 videos before it (screenshot at the top)
mutube analyze {YOUTUBE_VIDEO_URL}
```

## 🔄 Update
```bash
uv tool install --upgrade mutube
```

## Note:

This project is very much a work in progress. Expect bugs.
agent2agent-client 0.0.2
Agent2Agent server utility and boilerplate code from google.
Requires Python: >=3.10

# agent2agent-client
This repository is a wrapper for building google a2a client, without explicitly copying the code to the native development codebase. This repository support - a2a client core, - a2a client utils,  - a2a client types under the package called commons. Infact this package is direct lift and shift from google's repository.

# How to install
### clone this repository
* option-1
    - `git clone https://github.com/pavanjava/agent2agent-client.git`
    - `pip install -e .`
* option-2

    - `pip install agent2agent-client` from pypi.org

# How to use this?
```python
# streaming_echo_client.py (Modifications based on echo_client.py)
import asyncio
import logging
from uuid import uuid4

from common_client.client import A2AClient
from common_client.types import Message, TextPart, TaskStatusUpdateEvent, TaskArtifactUpdateEvent

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

SEARCH_SERVER_URL = "http://localhost:8001/search-a2a"


async def main():
    client = A2AClient(url=SEARCH_SERVER_URL)

    task_id = f"search-task-{uuid4().hex}"
    user_text = "Impact of AI on on Supply Chain and Shipping Lines"

    user_message = Message(role="user", parts=[TextPart(text=user_text)])

    send_params = {
        "id": task_id,
        "message": user_message,
    }

    try:
        logger.info(f"Sending task {task_id} to {SEARCH_SERVER_URL}...")

        # Use the client's send_task_streaming method
        response = await client.send_task(payload=send_params, timeout=300)
        print(response)
        if response.error:
            # Errors might be sent as part of the stream in some implementations
            logger.error(f"Received error in stream for task {task_id}: {response.error.message}")

        elif response.result:
            event = response.result
            if isinstance(event, TaskStatusUpdateEvent):
                logger.info(f"Task {task_id} Status Update: {event.status.state}")
                if event.status.message and event.status.message.parts:
                    part = event.status.message.parts[0]
                    if isinstance(part, TextPart):
                        logger.info(f"  Agent Message: {part.text}")
                if event.final:
                    logger.info(f"Task {task_id} reached final state.")

            elif isinstance(event, TaskArtifactUpdateEvent):
                logger.info(f"Task {task_id} Artifact Update: {event.artifact.name}")
                # Process artifact parts...
            else:
                logger.warning(f"Received unknown event type : {type(event)}")
        else:
            logger.error(f"Received unexpected empty response for task {task_id}")


    except Exception as e:
        logger.error(f"An error occurred during task communication: {e}")


if __name__ == "__main__":
    asyncio.run(main())

```
AreaNinja 0.1.4
Library for calculating the area of geometric shapes
Requires Python: >=3.7


# AreaNinja 

Быстро и точно, как ниндзя, считает площади 

---

## Возможности

- Вычисление площади круга по радиусу.
- Вычисление площади треугольника по трём сторонам (формула Герона).
- Вычисление площади прямоугольника.
- Проверка, является ли треугольник прямоугольным.
- Лёгкое расширение для других фигур.

---

## Установка

```bash
pip install AreaNinja
```

# Быстрый старт
```python
from AreaNinja import Circle, Triangle

# Площадь круга
c = Circle(5)
print(c.area())  # 78.53981633974483

# Площадь треугольника
t = Triangle(3, 4, 5)
print(t.area())  # 6.0

# Проверка на прямоугольность
print(t.is_right_angled())  # True
```
# Требования
Python 3.7+

Библиотека math (входит в стандартную библиотеку Python)

# Тестирование
Тесты находятся в папке tests/.

Для запуска всех тестов:

```bash

python -m unittest discover tests
```

# Лицензия
Этот проект распространяется под лицензией MIT.

# Автор
Kirill Domitin — domitin@gmail.com
mcp-google-analytics 0.0.3
MCP server for Google Analytics integration
Requires Python: >=3.10

# Google Analytics MCP Server

A Model Context Protocol (MCP) server for Google Analytics integration. This server provides tools for interacting with Google Analytics, including running reports, querying accounts and properties, and accessing metadata.

## Features

- **Run Reports**: Get analytics data with specified metrics and dimensions
- **Account Management**: List accounts and properties
- **Metadata Access**: Get information about available metrics and dimensions
- **Resources**: Access Google Analytics accounts, properties, and metadata
- **Prompts**: Templates for common Google Analytics workflows

## Installation

```bash
pip install mcp-google-analytics
```

## Configuration

Set the following environment variables:

```bash
export GOOGLE_ANALYTICS_CLIENT_ID="your_client_id"
export GOOGLE_ANALYTICS_CLIENT_SECRET="your_client_secret"
export GOOGLE_ANALYTICS_REFRESH_TOKEN="your_refresh_token"
export GOOGLE_ANALYTICS_PROPERTY_ID="your_property_id"
```

### OAuth Setup

To get the required OAuth credentials:

1. Go to the [Google Cloud Console](https://console.cloud.google.com/)
2. Create a new project or select an existing one
3. Enable the Google Analytics Data API and Google Analytics Admin API
4. Create OAuth 2.0 credentials
5. Use the OAuth 2.0 Playground to get a refresh token:
   - Go to https://developers.google.com/oauthplayground/
   - Configure the OAuth 2.0 Playground to use your client ID and secret
   - Select the required Google Analytics scopes
   - Click "Authorize APIs"
   - Exchange the authorization code for tokens
   - Save the refresh token

## Usage

### Starting the server directly

```bash
mcp-google-analytics
```

### Using with Claude Desktop

Add the following to your claude_desktop_config.json file:

```json
"mcp-google-analytics": {
  "command": "uvx",
  "args": [
    "mcp-google-analytics"
  ],
  "env": {
    "GOOGLE_ANALYTICS_CLIENT_ID": "your_client_id",
    "GOOGLE_ANALYTICS_CLIENT_SECRET": "your_client_secret",
    "GOOGLE_ANALYTICS_REFRESH_TOKEN": "your_refresh_token",
    "GOOGLE_ANALYTICS_PROPERTY_ID": "your_property_id"
  }
}
```

Replace the environment variables with your actual Google Analytics credentials.

## Available Tools

* **run_report**: Run a basic report with specified metrics and dimensions
* **get_accounts**: List all Google Analytics accounts
* **get_properties**: List all properties within an account
* **get_metadata**: Get metadata about available metrics and dimensions

## Available Resources

* **google_analytics://accounts**: List of all accounts
* **google_analytics://properties/{account_id}**: List properties for a specific account
* **google_analytics://metadata**: Metadata about available metrics and dimensions

## Available Prompts

* **create_basic_report**: Template for creating a basic report
* **analyze_traffic_sources**: Template for analyzing traffic sources
* **track_conversions**: Template for tracking conversions

## Version

0.0.1
mcp-jira 0.0.2
MCP server for Jira integration
Requires Python: >=3.10

# Jira MCP Server

A Model Context Protocol (MCP) server for Jira integration. This server provides tools for interacting with Jira, including creating, reading, and updating issues.

## Features

- **Issue Management**: Create, read, update, and transition issues
- **Search**: Use JQL to search for issues
- **Comments**: Add comments to issues
- **Resources**: Access metadata about projects, issue types, and priorities
- **Prompts**: Templates for common Jira workflows

## Installation

```bash
pip install mcp-jira
```

## Configuration

Set the following environment variables:

```bash
export JIRA_BASE_URL="https://your-instance.atlassian.net"
export JIRA_USERNAME="your_email@example.com"
export JIRA_API_TOKEN="your_api_token"
```

You can get an API token from: https://id.atlassian.com/manage-profile/security/api-tokens

## Usage

Start the server:

```bash
mcp-jira
```
### Using with Claude Desktop

Add the following to your `claude_desktop_config.json` file:

```json
"mcp-jira": {
  "command": "uvx",
  "args": [
    "mcp-jira"
  ],
  "env": {
    "JIRA_BASE_URL": "https://your-instance.atlassian.net",
    "JIRA_USERNAME": "your_email@example.com",
    "JIRA_API_TOKEN": "your_api_token"
  }
}
```

Replace the environment variables with your actual Jira API credentials.

## Available Tools

- **get_issue**: Get details of a specific Jira issue
- **search_issues**: Search for Jira issues using JQL
- **create_issue**: Create a new Jira issue
- **update_issue**: Update an existing Jira issue
- **add_comment**: Add a comment to a Jira issue
- **transition_issue**: Change the status of a Jira issue

## Available Resources

- **jira://projects**: List of all Jira projects
- **jira://issuetypes**: List of all issue types
- **jira://priorities**: List of all priorities

## Available Prompts

- **create_bug**: Template for creating a bug report
- **jql_search**: Template for creating JQL search queries

## Version

0.0.1