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MCP Server generated by mcp.ag2.ai
Configuration
View docs{
"mcpServers": {
"ag2-mcp-servers-transport-department-rajasthan": {
"command": "python",
"args": [
"mcp_server/main.py",
"stdio"
],
"env": {
"CONFIG": "YOUR_VALUE",
"SECURITY": "YOUR_VALUE",
"CONFIG_PATH": "YOUR_VALUE"
}
}
}
}This MCP (Model Context Protocol) Server hosts a specific OpenAPI-based context for the Transport Department in Rajasthan, enabling clients to interact with the provider’s data and actions through a standardized MCP interface. It is designed to run locally or in your environment, and it supports multiple transport modes for loading and consuming data via MCP clients.
You connect to this MCP server from your MCP client to perform defined transport-ready actions. Start the local server using the runtime that fits your environment, and then point your client at the server’s MCP URL or stream. Use the provided environment configuration to tailor security and the location of your configuration. When the server is running, use your MCP client to request available operations, fetch data, or trigger actions exposed by the server.
Prerequisites: Python 3.9+ and the Python package manager you use (pip). You also need uv to run the development server if you choose the UV-based workflow.
pip install -e ".[dev]"Alternative: install via UVX if you prefer the UV-based workflow.
uv pip install --editable ".[dev]"Running the MCP server directly from your environment is supported. Use the standard Python runtime to launch the server so it serves in stdio mode for local development. The command shown runs the server from its main entry point and enables you to interact with it through standard input/output.
python mcp_server/main.py stdioConfigure runtime behavior and security with environment variables. The following variables are used to locate and control the server configuration while running in production or development.
CONFIG_PATH=/path/to/mcp_config.json
CONFIG='{"exampleKey":"exampleValue"}'
SECURITY=YOUR_SECURITY_PARAMSThe project uses several tooling utilities to maintain quality and automate workflows. These tools help you lint, format, analyze, test, and build the MCP server.
Check code quality and formatting using ruff, ensuring codebase consistency and style compliance.
Automatically format code with ruff to maintain a uniform style across the project.
Run static analysis tools (mypy, bandit, semgrep) to detect type errors, security issues, and code smells.
Execute tests with pytest and produce coverage reports to validate functionality.
Build the MCP server artifact using Hatch for packaging and distribution.