tf-mcp-server

softengrahmed/tf-mcp-server

3.2

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The Model Context Protocol (MCP) server is a framework designed to facilitate the management and deployment of resources using Python-based tools, integrated with Terraform state files, and configured through a command-line interface.

Tools

  1. import_deployed_resources

    Imports deployed resources from a specific environment.

  2. read_lastdeployed_tfstates

    Reads the last-deployed Terraform state files.

  3. read_imported_tfstates

    Reads imported Terraform state files.

Setup Procedure for MCP Server Python Code

This guide covers steps required for setting up a Python project for MCP Server, writing MCP server tools, initializing the MCP environment, and correctly installing and configuring the MCP server.

Pre-requisites

Ensure the following are installed before starting:

  1. Python:

    • Install Python (version 3.7 or above recommended).
    • Verify the installation: 
      python --version

  2. UV (Node.js-based tools):

    • Install UV globally using Node.js: 
      npm install -g uv
    • Verify the installation: 
      uv --version

Step 1: Create a Python MCP Project

a. Create a Python Project

  1. Create a directory for your MCP Server tools: mkdir mcp-server-python cd mcp-server-python

  2. Initialize a Python virtual environment (additional details in Step 2).

b. Write Python Code for MCP Server Tools

Write Python modules in the project directory. Ensure the following tools are defined:

  • import_deployed_resources.py: For importing deployed resources.
  • read_lastdeployed_tfstates.py: For reading the last-deployed Terraform state files.
  • read_imported_tfstates.py: For reading imported Terraform state files.

Example code template for defining an MCP tool: from mcp import tool

@tool(name="import_deployed_resources") def import_deployed_resources(param1, param2): """ Example: Imports deployed resources from a specific environment. """ # Your logic here return {"status": "success"}

Step 2: MCP Environment Setup

Activate and prepare the MCP environment:

a. Run uv init

Initialize the MCP project: uv init

Output: Initialized project 'map-server'.

b. Create and Activate Virtual Environment

  1. Create a virtual environment: uv venv

  2. Activate the virtual environment:

  • Mac/Linux: 
    source .venv/bin/activate
  • Windows: 
    .venv\Scripts\activate
c. Install MCP to the Virtual Environment

Install MCP CLI into your virtual environment: uv add "mcp[cli]"

Step 3: Testing MCP Server Code

(Optional) Run your MCP Server tools in development mode to test functionality: mcp dev mcp-server-code.py

Step 4: Installing MCP Server Configuration

  1. Install the MCP server configuration using MCP CLI: mcp install mcp-server-code.py

  2. Ensure the full path to your Python code (mcp-server-code.py) is added to the MCP configuration. Important: If any filename or folder structure changes, re-run the mcp install command to update the configuration.

  3. Customizing MCP Tool Names:

  • Update tool names in the Python code by changing:
    • @mcp.tool(name="tool-name") decorator
    • def tool_name(params): function name

Step 5: Verify MCP Server Installation

Once installed, your MCP server should appear in the Claude Desktop Application on the startup screen.

Troubleshooting Tips

  1. Verify Installed Packages: Ensure that the mcp[cli] package is installed in the virtual environment by running: uv list

This will display the installed modules.

  1. Reconfigure MCP Server: If there are any filename or folder name changes, re-run: mcp install mcp-server-code.py

Summary

Following these steps will enable you to:

  • Create a Python project for MCP server tools.
  • Set up the MCP environment correctly.
  • Register and configure your MCP server in the Claude Desktop Application.
  • Use Python to define and manage MCP tools with maximum flexibility.