Meraki

MCP Server generated by mcp.ag2.ai

python

GitHub Stars

python

Language

7 months ago

First Indexed

3 months ago

Catalog Refreshed

Documentation & install

Readme and setup notes from the catalogue, plus a client-ready config you can copy for your MCP host.

Installation

Add the following to your MCP client configuration file.

Configuration

View docs

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

You run an MCP server that exposes a Meraki OpenAPI-based interface locally or in your environment. It enables clients to interact with the API through a single, consistent MCP protocol layer while you keep full control over configuration and transport. This guide shows practical steps to install, start, and operate the Meraki MCP Server so clients can request data and perform actions through the MCP channel.

How to use

Launch the MCP server in stdio mode to communicate with local clients. You will start the server once it is installed and configured, and then connect your MCP client to the standard input/output channel exposed by the process. Use this setup to send requests that your client understands and receive responses from the Meraki API surface provided by the MCP layer.

How to install

Prerequisites include Python 3.9 or newer, plus package and runtime tooling invoked during setup.

Additional setup and notes

Configuration parameters are provided via environment variables when you start the server. You can point the server at a JSON configuration file or supply a JSON string directly. Security parameters can also be supplied through environment variables to control access to the MCP server.

Available tools

lint

Check code for issues and enforce styling with ruff; also formats code with ruff format.

static-analysis

Run static analysis including mypy, bandit, and semgrep via a dedicated script.

tests

Execute tests with pytest to validate functionality and generate coverage reports.

build

Build and publish the package, typically using Hatch tools.

pre-commit

Run pre-commit hooks to ensure code quality before commits.

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