semvec/pypi-packages huggingface dataset:
iflow-mcp_cbi-mcp-server 0.1.1
MCP Server for interacting with ChatCBI
Requires Python: >=3.12

# CB Insights MCP Server

The CBI MCP Server provides an interface for developers to interact with CB Insights ChatCBI LLM through AI Agents.

## Tools

### ChatCBI
Send a message from an agent to ChatCBI and return the response.
- Input parameters:
  - `message`: The content of your message to ChatCBI
  - `chatID`(optional): A unique identifier for the chat session, obtained from a previous response. If included, the conversation is continued. Otherwise, a new conversation is started.
- Returns JSON object containing the following fields:
  - `chatID`: Identifies the conversation. If chatID was provided in the request, this will be the same.
  - `message`: ChatCBI response to the message.
  - `relatedContent`: List of related references.
  - `sources`: List of sources used to generate the response.
  - `suggestions`: List of suggested follow-up questions.
  - `title`: Title of the chat
- For more information see [Using ChatCBI](https://api-docs.cbinsights.com/portal/docs/CBI-API/chatcbi)

## Setup
The CBI MCP Server uses [uv](https://docs.astral.sh/uv/getting-started/installation/) to manage the project. 

Environment variables are set via the .env file:
- `CBI_CLIENT_ID` & `CBI_CLIENT_SECRET` OAuth Client ID and Secret
  - see [CB Insights API Authentication](https://api-docs.cbinsights.com/portal/docs/CBI-API/Authentication) for usage and how to obtain them
- `CBI_MCP_TIMEOUT` (default: 120)
- `CBI_MCP_PORT` (default: 8000)

## Usage

### With Claude Desktop

Update the `claude_desktop_config.json` file using the following command:

```shell
mcp install server.py
```

This will add the following configuration:

```json
{
  "mcpServers": {
    "cbi-mcp-server": {
      "command": "/path/to/.local/bin/uv",
      "args": [
        "--directory",
        "/path/to/cloned/cbi-mcp-server",
        "run",
        "server.py"
      ]
    }
  }
}
```

## Debugging

The [inspector](https://modelcontextprotocol.io/docs/tools/inspector#getting-started) can be used to test/debug your server. 

```shell
mcp dev server.py 
```
[More info on using the inspector](https://modelcontextprotocol.io/docs/tools/inspector#py-pi-package)
wandb-tools 0.1.2
Add your description here
Requires Python: >=3.12

# Wandb-Local

## Server

### 1. Launch Server
```bash
python3 serve.py --project <project name> --port <port>
```
- 建立名稱為 `<user name>-wandb-<project name>` container
- 預設的存儲目錄是 `/volume/share/wandb-local/volume/<project name>`

若要使用預設路徑外的儲存目錄，可以指定 `--volume` 參數，例如
```bash
python3 serve.py --project <project name> --port <port> --volume /volume/share/wandb-local/volume/trade-richardwang/
```

### 2. Registration
1. 瀏覽器打開 `http://<host ip>:<port>`
   - `<host ip>` 為當初執行 server 啟動程式的機器的 IP，例如 10.11.60.1
2. 點擊 login 並註冊此部屬上的帳戶，建議設置:
   - Full name: Sinopac  
   - E-mail: 123@sinopac.com
   - Username: sinopac
   - Pssword: 123@sinopac.com

## Client

### Configure connection
<api-key> 可在右上角機器人頭像 -> User Settings -> Danger Zone -> API keys -> Reveal 中找到

#### 寫在 dot env 檔案
設置於 `.env` 檔案
```
WANDB_BASE_URL=http:/<host ip>:<port>
WANDB_API_KEY=<api-key>
```

#### 寫在 Python 檔案
```python
os.environ["WANDB_BASE_URL"] = "http:/<host ip>:<port>"
os.environ["WANDB_API_KEY"] = "<api-key>"
```

### Best practices

#### Programmatically cleanup cache
wandb use `./cache/local` to save artifacts even they are uploaded to server.
```python
c = wandb.wandb_sdk.wandb_artifacts.get_artifacts_cache()
c.cleanup(int(10e9)) # up to 10GB cache
```

#### Do not log system metrics
System metrics are frequent and many, which can put a lot of pressure on a local server.
```python
wandb.init(
    ...
    settings=wandb.Settings(
        _disable_stats=True,
        _disable_machine_info=True,
    )
)
```