[build-system]
requires = ["hatchling"]
build-backend = "hatchling.build"

[project]
name = "golden-dataset-mcp"
version = "0.1.2"
description = "MCP server wrapping golden-dataset-studio for version-controlled golden dataset management and RAG evaluation"
readme = "README.md"
license = "MIT"
requires-python = ">=3.10"
authors = [
    { name = "Nipun Khanderia" }
]
keywords = ["mcp", "model-context-protocol", "rag", "evaluation", "golden-dataset", "llm-testing"]
classifiers = [
    "Development Status :: 4 - Beta",
    "Intended Audience :: Developers",
    "License :: OSI Approved :: MIT License",
    "Programming Language :: Python :: 3",
    "Programming Language :: Python :: 3.10",
    "Programming Language :: Python :: 3.11",
    "Programming Language :: Python :: 3.12",
    "Topic :: Software Development :: Testing",
    "Topic :: Software Development :: Quality Assurance",
]

dependencies = [
    "fastmcp>=2.0.0",
    "golden-dataset-studio>=0.1.0",
    "pydantic>=2.0.0",
    # golden-dataset-studio's evaluate_dataset() requires scikit-learn at
    # runtime but does not declare it as a dependency (as of 0.1.1) — pinned
    # here explicitly so evaluate_answers works out of the box.
    "scikit-learn>=1.3.0",
]

[project.optional-dependencies]
dev = [
    "pytest>=8.0.0",
    "pytest-asyncio>=0.24.0",
    "build>=1.0.0",
    "twine>=5.0.0",
]

[project.urls]
Homepage = "https://github.com/nipunkhanderia/golden-dataset-mcp"
Repository = "https://github.com/nipunkhanderia/golden-dataset-mcp"
Issues = "https://github.com/nipunkhanderia/golden-dataset-mcp/issues"

[project.scripts]
golden-dataset-mcp = "golden_dataset_mcp.server:main"

[tool.hatch.build.targets.wheel]
packages = ["src/golden_dataset_mcp"]

[tool.pytest.ini_options]
testpaths = ["tests"]
asyncio_mode = "auto"