Metadata-Version: 2.4
Name: artl-mcp
Version: 0.16.0
Summary: PydanticAI and MCP approaches for getting textual representations of scientific literature from PMIDs, DOIs, etc.
Project-URL: Homepage, https://github.com/contextualizer-ai/artl-mcp
Project-URL: Repository, https://github.com/contextualizer-ai/artl-mcp
Project-URL: Issues, https://github.com/contextualizer-ai/artl-mcp/issues
Project-URL: Documentation, https://github.com/contextualizer-ai/artl-mcp#readme
Author-email: Mark Andrew Miller <MAM@lbl.gov>, Justin Reese <justaddcoffee@gmail.com>
License: MIT
License-File: LICENSE
Keywords: doi,mcp,pmid,pubmed,scientific-literature
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3.11
Requires-Dist: beautifulsoup4>=4.9.0
Requires-Dist: fastmcp>=2.8.1
Requires-Dist: lxml>=4.6.0
Requires-Dist: pdfminer-six>=20250506
Requires-Dist: pydantic>=2.0.0
Requires-Dist: requests>=2.25.0
Description-Content-Type: text/markdown

# All Roads to Literature

An MCP for retrieving scientific literature metadata and content using PMIDs, DOIs, and other identifiers.

## Features

- Retrieve metadata for scientific articles using DOIs
- Fetch abstracts from PubMed using PMIDs
- MCP-based architecture for easy extensibility

## Installation

### Prerequisites

- Python 3.11 or higher
- uv (optional but recommended)

### Setup

1. Clone the repository:
   ```bash
   git clone https://github.com/yourusername/all-roads-to-literature.git
   cd all-roads-to-literature
   ```

2. Install with uv (recommended):
   ```bash
   uv venv
   uv pip install -e .
   ```

   Or with standard pip:
   ```bash
   python -m venv .venv
   source .venv/bin/activate  # On Windows: .venv\Scripts\activate
   pip install -e .
   ```

## Usage

### Starting the MCP Server

To start the MCP server:

```bash
uv run artl --server
```

This will start the server in the current terminal. The server provides access to all registered tools through FastMCP's interface.

### Running the Tests

```bash
uv run pytest tests/
```

## Architecture

The project follows this structure:

- `main.py`: Entry point that creates and configures the MCP server
- `tools.py`: Contains the tool implementations that the MCP server exposes


