Metadata-Version: 2.4
Name: deepset-mcp
Version: 0.0.8
Summary: Collection of MCP tools and Agents to work with the deepset AI platform. Create, debug or learn about pipelines on the platform. Useable from the CLI, Cursor, Claude Code, or other MCP clients.
Project-URL: Homepage, https://deepset.ai
Author-email: Mathis Lucka <mathis.lucka@deepset.ai>, Tanay Soni <tanay.soni@deepset.ai>
License-Expression: Apache-2.0
License-File: LICENSE
Keywords: Agents,Haystack,LLM,MCP,deepset,pipelines
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: License :: Freely Distributable
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.11
Requires-Dist: fastapi
Requires-Dist: glom
Requires-Dist: httpx
Requires-Dist: mcp>=1.10.1
Requires-Dist: model2vec
Requires-Dist: numpy
Requires-Dist: orjson
Requires-Dist: pydantic>=2.0.0
Requires-Dist: pyjwt[crypto]
Requires-Dist: pyyaml
Requires-Dist: rich
Requires-Dist: typer
Provides-Extra: redis
Requires-Dist: redis>=4.0.0; extra == 'redis'
Description-Content-Type: text/markdown

# deepset-mcp

**The official MCP server and Python SDK for the deepset AI platform**

deepset-mcp enables AI agents to build and debug pipelines on the [deepset AI platform](https://www.deepset.ai/products-and-services/deepset-ai-platform) through 30+ specialized tools. It also provides a Python SDK for programmatic access to many platform resources.

## Documentation

📖 **[View the full documentation](https://deepset-ai.github.io/deepset-mcp-server/)**

## Quick Links

- 🔗 **[deepset AI Platform](https://www.deepset.ai/products-and-services/deepset-ai-platform)**
- 📚 **[Installation Guide](https://deepset-ai.github.io/deepset-mcp-server/installation/)**
- 🛠️ **[MCP Server Guide](https://deepset-ai.github.io/deepset-mcp-server/guides/mcp_server/)**
- 🐍 **[Python SDK Guide](https://deepset-ai.github.io/deepset-mcp-server/guides/api_sdk/)**

## Development

### Installation

Install the project using [uv](https://docs.astral.sh/uv/):

```bash
# Install uv first
pipx install uv

# Install project with all dependencies
uv sync --locked --all-extras --all-groups
```

### Code Quality & Testing

Run code quality checks and tests using the Makefile:

```bash
# Install dependencies
make install

# Code quality
make lint          # Run ruff linting
make format        # Format code with ruff
make types         # Run mypy type checking

# Testing
make test          # Run unit tests (default)
make test-unit     # Run unit tests only
make test-integration     # Run integration tests
make test-all      # Run all tests

# Clean up
make clean         # Remove cache files
```

### Documentation

Documentation is built using [MkDocs](https://www.mkdocs.org/) with the Material theme:

- Configuration: `mkdocs.yml`
- Content: `docs/` directory
- Auto-generated API docs via [mkdocstrings](https://mkdocstrings.github.io/)
- Deployed via GitHub Pages (automated via GitHub Actions on push to main branch)

