Provides integration with Microsoft Cognitive Services Training API, enabling access to training machine learning models for Microsoft's cognitive services.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Custom Vision Training Client train a new model to classify dog breeds using my uploaded images"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
This project is an MCP (Model Context Protocol) Server for the given OpenAPI URL - https://api.apis.guru/v2/specs/microsoft.com/cognitiveservices-Training/3.2/openapi.json, auto-generated using AG2's MCP builder.
Python 3.9+
pip and uv
Related MCP server: SensorMCP Server
Clone the repository:
git clone <repository-url>
cd mcp-serverInstall dependencies:
The .devcontainer/setup.sh script handles installing dependencies using pip install -e ".[dev]". If you are not using the dev container, you can run this command manually.
pip install -e ".[dev]"Alternatively, you can use uv:
uv pip install --editable ".[dev]"This project uses ruff for linting and formatting, mypy for static type checking, and pytest for testing.
To check for linting issues:
ruff checkTo format the code:
ruff formatThese commands are also available via the scripts/lint.sh script.
To run static analysis (mypy, bandit, semgrep):
./scripts/static-analysis.shThis script is also configured as a pre-commit hook in .pre-commit-config.yaml.
To run tests with coverage:
./scripts/test.shThis will run pytest and generate a coverage report. For a combined report and cleanup, you can use:
./scripts/test-cov.shThis project uses pre-commit hooks defined in .pre-commit-config.yaml. To install the hooks:
pre-commit installThe hooks will run automatically before each commit.
The MCP server can be started using the mcp_server/main.py script. It supports different transport modes (e.g., stdio, sse, streamable-http).
To start the server (e.g., in stdio mode):
python mcp_server/main.py stdioThe server can be configured using environment variables:
CONFIG_PATH: Path to a JSON configuration file (e.g., mcp_server/mcp_config.json).
CONFIG: A JSON string containing the configuration.
SECURITY: Environment variables for security parameters (e.g., API keys).
Refer to the if __name__ == "__main__": block in mcp_server/main.py for details on how these are loaded.
The tests/test_mcp_server.py file demonstrates how to start and interact with the server programmatically for testing.
This project uses Hatch for building and publishing. To build the project:
hatch buildTo publish the project:
hatch publishThese commands are also available via the scripts/publish.sh script.
This server cannot be deployed
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