semvec/pypi-packages huggingface dataset:
langchain-mcp 0.2.1
Model Context Protocol tool calling support for LangChain
Requires Python: >=3.10

# langchain-mcp

![PyPI - Version](https://img.shields.io/pypi/v/langchain-mcp)

[Model Context Protocol](https://modelcontextprotocol.io) tool calling support in LangChain.

Create a `langchain_mcp.MCPToolkit` with an `mcp.ClientSession`,
then `await toolkit.initialize()` and `toolkit.get_tools()` to get the list of `langchain_core.tools.BaseTool`s.

Example:

https://github.com/rectalogic/langchain-mcp/blob/8fa8445a24755bf91789f52718c32361ed916f46/tests/demo.py#L34-L43

## Demo

You can run the demo against [Groq](https://groq.com/) `llama-3.1-8b-instant`:
```sh-session
$ export GROQ_API_KEY=xxx
$ uv run tests/demo.py "Read and summarize the file ./LICENSE"
Secure MCP Filesystem Server running on stdio
Allowed directories: [ '/users/aw/projects/rectalogic/langchain-mcp' ]
The file ./LICENSE is a MIT License agreement. It states that the software is provided "as is" without warranty and that the authors and copyright holders are not liable for any claims, damages, or other liability arising from the software or its use.
```
dataflowtool 1.0.1
A modern data lineage visualization tool with column-level dependency tracking
Requires Python: >=3.7
Keywords: data lineage, dbt, visualization, data dependencies, column lineage

# Data Flow Tool

A modern data lineage visualization tool that helps you understand column-level dependencies between your data tables.

## Features

- Interactive visualization of data lineage
- Column-level dependency tracking
- Support for DBT projects
- Dark/Light mode
- Interactive column highlighting
- Draggable nodes
- Detailed tooltips with metadata

## Installation

```bash
pip install dataflowtool
```

## Usage

1. From the command line:
```bash
dataflowtool
```
This will start the server and open the visualization in your default web browser.

2. As a Python module:
```python
from dataflowtool.dataflowtool.server import run_server

run_server()
```

## DBT Integration

The tool automatically detects DBT projects in the current directory or its parents. If a DBT project is found, it will use the manifest.json and catalog.json files to generate the lineage visualization.

If no DBT project is found, the tool will use sample data to demonstrate its functionality.

## Development

1. Clone the repository:
```bash
git clone https://github.com/yourusername/dataflowtool.git
cd dataflowtool
```

2. Install dependencies:
```bash
pip install -e .
cd frontend
npm install
```

3. Build the frontend:
```bash
npm run build
```

4. Run the development server:
```bash
python dataflowtool/server.py
```

## License

MIT License