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API Isendpro MCP Server

MCP Server generated by mcp.ag2.ai

Installation

Add the following to your MCP client configuration file.

Configuration

{
  "mcpServers": {
    "ag2-mcp-servers-api-isendpro": {
      "command": "python",
      "args": [
        "mcp_server/main.py",
        "stdio"
      ],
      "env": {
        "CONFIG": "JSON string containing the configuration",
        "SECURITY": "Environment variables for security parameters (e.g., API keys)",
        "CONFIG_PATH": "Path to a JSON config file, e.g. mcp_server/mcp_config.json"
      }
    }
  }
}

This MCP Server provides a dedicated endpoint for the isendpro OpenAPI-based API, enabling clients to interact with the API through the Model Context Protocol. By running a local MCP server, you can test, integrate, and develop against the API in a consistent, context-aware environment.

How to use

Start the MCP server in stdio mode to run locally and interact with it from your MCP client. Use the standard Python command to launch the server, then connect your client to the running process. You can start, stop, and restart the server as part of your development workflow.

How to install

Prerequisites: Python 3.9+ and uv are available on your system.

1. Clone the project directory and navigate into it.

git clone <repository-url>
cd mcp_server

2. Install development dependencies. You can use either of the following approaches.

pip install -e ".[dev]"
```
 or
```
uv pip install --editable ".[dev]"

3. (Optional) Run static checks and formatting locally using the provided tooling.

ruff check
ruff format
./scripts/static-analysis.sh
pre-commit install

4. Start the MCP server in stdio mode.

python mcp_server/main.py stdio

Additional content

Configuration can be provided via environment variables. The setup supports multiple ways to load configuration, including a JSON file path or a JSON string.

Security-related environment parameters can be supplied to control access and behavior, such as API keys or other security tokens. When you run the server, you can specify these values through the appropriate environment variables.

Development and testing workflows are supported through a unified script set. You can run tests with coverage, and you can generate a combined report for convenience.

Build and publish steps use the project’s packaging tool to create distributable artifacts and publish them to the intended registry.