semvec/pypi-packages huggingface dataset:
notification-mcp-shao 1.0.0
MCP server for email and DingTalk notifications
Requires Python: >=3.10

# Notification MCP Server

MCP 服务器，用于发送邮件和钉钉消息通知。

## 功能

- 📧 发送邮件（支持 SMTP）
- 🤖 发送钉钉消息（支持 Markdown 格式）

## 安装

使用 uvx 运行（推荐）：
```bash
uvx notification-mcp-server@latest
```

或使用 pip 安装：
```bash
pip install notification-mcp-server
```

## 配置

需要设置以下环境变量：

### 邮件配置
- `SMTP_SERVER`: SMTP 服务器地址（如 smtp.163.com）
- `SMTP_PORT`: SMTP 端口（如 465）
- `EMAIL_ACCOUNT`: 邮箱账号
- `EMAIL_PASSWORD`: 邮箱授权码
- `SMTP_SSL`: 是否使用 SSL（true/false）

### 钉钉配置
- `DINGTALK_WEBHOOK`: 钉钉机器人 Webhook URL
- `DINGTALK_SECRET`: 钉钉机器人加签密钥（可选）

## 在 Kiro IDE 中使用

在 `.kiro/settings/mcp.json` 中添加：

```json
{
  "mcpServers": {
    "notification": {
      "command": "uvx",
      "args": ["notification-mcp-server@latest"],
      "env": {
        "SMTP_SERVER": "smtp.163.com",
        "SMTP_PORT": "465",
        "EMAIL_ACCOUNT": "your@email.com",
        "EMAIL_PASSWORD": "your-auth-code",
        "SMTP_SSL": "true",
        "DINGTALK_WEBHOOK": "your-webhook-url"
      }
    }
  }
}
```

## 可用工具

### send_email
发送邮件到指定邮箱。

参数：
- `to`: 收件人邮箱地址
- `subject`: 邮件主题
- `content`: 邮件内容
- `content_type`: 内容类型（plain/html，默认 plain）
- `cc`: 抄送列表（可选）
- `bcc`: 密送列表（可选）

### send_dingtalk_message
发送消息到钉钉群。

参数：
- `title`: 消息标题
- `text`: 消息内容
- `msg_type`: 消息类型（text/markdown，默认 markdown）
- `at_mobiles`: @的手机号列表（可选）
- `at_all`: 是否@所有人（可选）

## 作者

@author shao

## 许可证

MIT
dbgear-doc 0.5.0
Documentation generator plugin for DBGear
Requires Python: <4.0,>=3.12

# dbgear-doc

Documentation generator plugin for [DBGear](https://github.com/tamuto/dbgear).

## Overview

dbgear-doc generates Markdown documentation from DBGear database schema definitions. It provides:

- Automatic documentation generation from `schema.yaml` files
- Table structure documentation with columns, types, and descriptions
- Index and foreign key relationship documentation
- Schema-level overview pages

## Installation

```bash
pip install dbgear-doc
```

Or with Poetry:

```bash
poetry add dbgear-doc
```

## Usage

Once installed, the `doc` subcommand becomes available in the dbgear CLI:

```bash
# Generate documentation (outputs to ./docs by default)
dbgear --project path/to/project doc

# Specify output directory
dbgear --project path/to/project doc -o ./documentation
```

### Programmatic Usage

```python
from dbgear_doc import DocumentGenerator, generate_docs
from dbgear.models.schema import SchemaManager
from pathlib import Path

# Simple usage
generate_docs("schema.yaml", "./docs")

# Advanced usage with SchemaManager
schema_manager = SchemaManager.load("schema.yaml")
generator = DocumentGenerator(schema_manager)
generated_files = generator.generate(Path("./docs"))
```

## Output Structure

```
docs/
├── index.md              # Main index with schema list
└── {schema_name}/
    ├── README.md         # Schema overview with table list
    └── {table_name}.md   # Individual table documentation
```

## Generated Documentation

Each table documentation includes:

- **Description**: Table notes/comments from schema
- **Columns**: Column name, type, nullable, default, and description
- **Primary Key**: Primary key columns
- **Indexes**: Index definitions with columns and uniqueness
- **Foreign Keys**: Foreign key constraints with references

## License

MIT License
anosys-sdk-openai-agents 1.0.1
AnoSys SDK for OpenAI Agents - Automatic instrumentation and logging for OpenAI Agents
Requires Python: >=3.10
Keywords: anosys, openai, agents, observability, monitoring, logging, llm

# AnoSys SDK for OpenAI Agents

[![License: Apache 2.0](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
[![Python](https://img.shields.io/badge/python-3.9%2B-blue.svg)](https://www.python.org/downloads/)

Automatically capture and send OpenAI Agents SDK traces to [AnoSys](https://anosys.ai) for monitoring, analytics, and observability.

## Features

✨ **Automatic Agent Tracing** - Captures all agent runs, tool calls, and handoffs  
✨ **TracingProcessor Integration** - Native integration with OpenAI Agents SDK  
✨ **Structured Span Data** - Captures agent, function, guardrail, and generation spans  
✨ **User Context Support** - Associate traces with user sessions  

## Installation

```bash
pip install anosys-sdk-openai-agents
```

## Quick Start

```python
import os
from agents import Agent, Runner, set_tracing_processor
from anosys_sdk_openai_agents import AnosysOpenAIAgentsLogger

os.environ["OPENAI_API_KEY"] = "your-openai-key"
os.environ["ANOSYS_API_KEY"] = "your-anosys-key"

# Set up the tracing processor
set_tracing_processor(AnosysOpenAIAgentsLogger())

# Create and run an agent
agent = Agent(
    name="Assistant",
    instructions="You are a helpful assistant."
)

result = Runner.run_sync(agent, "Hello!")
print(result.final_output)
```

## User Context

Associate traces with user sessions:

```python
def get_user_context():
    return {
        "session_id": "user-123",
        "token": "auth-token"
    }

processor = AnosysOpenAIAgentsLogger(get_user_context=get_user_context)
set_tracing_processor(processor)
```

## Span Types Captured

- **agent** - Agent execution spans
- **function** - Tool/function calls
- **generation** - LLM generation calls
- **guardrail** - Guardrail checks
- **handoff** - Agent handoffs
- **response** - Response spans
- **custom** - Custom spans

## Environment Variables

| Variable | Required | Description |
|----------|----------|-------------|
| `ANOSYS_API_KEY` | Yes | Your AnoSys API key |
| `OPENAI_API_KEY` | Yes | Your OpenAI API key |

## License
Apache 2.0
anosys-sdk-openai 1.0.1
AnoSys SDK for OpenAI - Automatic instrumentation and logging for OpenAI API calls
Requires Python: >=3.10
Keywords: anosys, openai, observability, monitoring, logging, llm, gpt

# AnoSys SDK for OpenAI

[![License: Apache 2.0](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
[![Python](https://img.shields.io/badge/python-3.9%2B-blue.svg)](https://www.python.org/downloads/)

Automatically capture and send OpenAI API calls to [AnoSys](https://anosys.ai) for monitoring, analytics, and observability.

## Features

✨ **Automatic OpenAI Instrumentation** - Captures all OpenAI API calls via OpenTelemetry  
✨ **Streaming Support** - Detects and logs streaming responses  
✨ **OpenTelemetry Semantic Conventions** - Follows Gen AI standards  
✨ **Error Tracking** - Captures exceptions with full stack traces  
✨ **Zero Configuration** - Works out of the box with just your API key  

## Installation

```bash
pip install anosys-sdk-openai
```

## Quick Start

```python
import os
from openai import OpenAI
from anosys_sdk_openai import AnosysOpenAILogger

os.environ["OPENAI_API_KEY"] = "your-openai-key"
os.environ["ANOSYS_API_KEY"] = "your-anosys-key"

# Initialize (do once at startup)
AnosysOpenAILogger()

# Use OpenAI normally - all calls are automatically logged
client = OpenAI()

response = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Hello!"}
    ]
)
print(response.choices[0].message.content)
```

## Streaming

Streaming is automatically detected and logged:

```python
stream = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[{"role": "user", "content": "Write a haiku"}],
    stream=True
)

for chunk in stream:
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="")
```

## Custom Function Logging

You can also log custom functions using decorators from the core package:

```python
from anosys_sdk_core import anosys_logger

@anosys_logger(source="my_app")
def process_response(response):
    return response.choices[0].message.content
```

## What Data is Captured?

Following [OpenTelemetry Gen AI standards](https://opentelemetry.io/docs/specs/semconv/gen-ai/):

- `gen_ai.system` - Always "openai"
- `gen_ai.request.model` - Model requested
- `gen_ai.response.model` - Model that responded
- `gen_ai.request.temperature` - Temperature parameter
- `gen_ai.usage.input_tokens` - Input token count
- `gen_ai.usage.output_tokens` - Output token count
- Request/response messages
- Timestamps and duration
- Error details

## Environment Variables

| Variable | Required | Description |
|----------|----------|-------------|
| `ANOSYS_API_KEY` | Yes | Your AnoSys API key |
| `OPENAI_API_KEY` | Yes | Your OpenAI API key |

## License

Apache 2.0
anosys-sdk-core 1.0.1
Core utilities for AnoSys SDK - AI observability and monitoring
Requires Python: >=3.10
Keywords: anosys, observability, monitoring, logging, ai, llm

# AnoSys SDK Core

Core utilities for the AnoSys SDK - shared functionality for all AnoSys integrations.

## Installation

```bash
pip install anosys-sdk-core
```

## Usage

### Function Decorators

```python
from anosys_sdk_core import anosys_logger, anosys_raw_logger, setup_api
import os

os.environ["ANOSYS_API_KEY"] = "your-api-key"

# Decorator for automatic logging
@anosys_logger(source="my_app")
def my_function(data):
    return process(data)

# Async functions work too
@anosys_logger(source="my_app.async")
async def my_async_function(data):
    return await async_process(data)

# Raw logging
anosys_raw_logger({
    "event": "custom_event",
    "data": {"key": "value"}
})
```

## API Reference

- `anosys_logger(source=None)` - Decorator to log function calls
- `anosys_raw_logger(data)` - Log arbitrary data directly
- `setup_api(path=None)` - Configure API endpoint

## License

Apache 2.0