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stellastra_mcp

MCP Server generated by mcp.ag2.ai

Installation

Add the following to your MCP client configuration file.

Configuration

{
  "mcpServers": {
    "ag2-mcp-servers-stellastra": {
      "command": "python",
      "args": [
        "mcp_server/main.py",
        "stdio"
      ],
      "env": {
        "CONFIG": "{...} (JSON config string)",
        "SECURITY": "YOUR_SECURITY_TOKEN_OR_KEYS",
        "CONFIG_PATH": "PATH_TO_CONFIG.json"
      }
    }
  }
}

You run an MCP (Model Context Protocol) Server to expose a configurable, programmatic interface for a given OpenAPI description, enabling clients to interact with the model context through standardized MCP transports. This server acts as a bridge between OpenAPI-defined capabilities and MCP clients, making it easier to integrate with tooling that relies on MCP for communication, discovery, and actions.

How to use

Start the MCP server in stdio mode to connect with an MCP client running in the same process or in a compatible environment. The server reads its configuration from environment variables or a JSON file and exposes the MCP interface for the configured API.

How to install

Prerequisites: Python 3.9 or newer, and either pip or uv to install dependencies and run the server.

1) Clone the repository and navigate to the server directory.

git clone <repository-url>
cd mcp-server

2) Install development dependencies.

pip install -e ".[dev]"

Alternatively, use uv to install editable development dependencies.

uv pip install --editable ".[dev]"

3) Run the MCP server in stdio mode.

python mcp_server/main.py stdio

Configuration and security (env vars you can set)

The server supports configuring its behavior via environment variables. Use the following keys to customize your setup.

- CONFIG_PATH: Path to a JSON configuration file (for example, mcp_server/mcp_config.json).

- CONFIG: A JSON string containing the configuration.

- SECURITY: Environment variables for security parameters (such as API keys or tokens).

Available tools

lint

Check code for style and potential issues using a linter to enforce consistency and quality.

format

Automatically format code for readability and style using a formatter.

static-analysis

Run static analysis tools to catch potential vulnerabilities and type issues.

tests

Execute the test suite to verify functionality and regression safety.

pre-commit

Hooked checks that run before each commit to ensure code quality and correctness.