Provides integration with Microsoft Cognitive Services Training API, enabling access to training machine learning models for Microsoft's cognitive services.
MCP Server
This project is an MCP (Multi-Agent Conversation 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.
Prerequisites
- Python 3.9+
- pip and uv
Installation
- Clone the repository:
- Install 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.Alternatively, you can useuv
:
Development
This project uses ruff
for linting and formatting, mypy
for static type checking, and pytest
for testing.
Linting and Formatting
To check for linting issues:
To format the code:
These commands are also available via the scripts/lint.sh script.
Static Analysis
To run static analysis (mypy, bandit, semgrep):
This script is also configured as a pre-commit hook in .pre-commit-config.yaml.
Running Tests
To run tests with coverage:
This will run pytest and generate a coverage report. For a combined report and cleanup, you can use:
Pre-commit Hooks
This project uses pre-commit hooks defined in .pre-commit-config.yaml. To install the hooks:
The hooks will run automatically before each commit.
Running the Server
The MCP server can be started using the mcp_server/main.py script. It supports different transport modes (e.g., stdio
, sse
).
To start the server (e.g., in stdio mode):
The 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.
Building and Publishing
This project uses Hatch for building and publishing. To build the project:
To publish the project:
These commands are also available via the scripts/publish.sh script.
This server cannot be installed
remote-capable server
The server can be hosted and run remotely because it primarily relies on remote services or has no dependency on the local environment.
An MCP server for Microsoft's Cognitive Services Custom Vision Training API that enables AI agents to create, train, and manage custom image classification and object detection models through natural language interactions.
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