How to Build an AI Agent That Reads and Writes Files Using MCP

This step-by-step guide explains how to build an AI agent that can safely read, write, and organize files using MCP’s filesystem server. Such an agent can be applied across multiple phases of the Software Development Life Cycle (SDLC), including requirements documentation, development workflows, test data and report management, deployment artifact handling, and operational file maintenance. The article focuses on the technical foundations needed to enable secure and controlled file system interactions using AI.

⚙️ Prerequisites

Before getting started, you should have:

• Basic understanding of AI agents and LLMs
• Claude Desktop installed
• Node.js installed (for MCP server usage)
• Familiarity with JSON configuration files
• A local directory you’re comfortable exposing to an AI agent

✅ Step 1: Understanding the Filesystem MCP Server

The Filesystem MCP Server provides a controlled interface that allows an AI agent to:

• Read files
• Write or update files
• List directory contents

Crucially, access is restricted to configured directories, which helps prevent unsafe or unintended file operations.

This server acts as a bridge between the AI agent and the local file system, enforcing boundaries defined by you.

✅ Step 2: Integrate Filesystem MCP Server with Claude AI

• Open Claude Desktop
• Navigate to:
  • Claude → Settings → Developer → Edit config
• Locate and open the file: claude_desktop_config.json
• Add Filesystem MCP Configuration

{
  "mcpServers": {
    "filesystem": {
      "command": "npx",
      "args": [
        "-y",
        "@modelcontextprotocol/server-filesystem",
        "/Users/yourname/projects/ai-agent-workspace"
      ]
    }
  }
}

{
  "mcpServers": {
    "filesystem": {
      "command": "npx",
      "args": [
        "-y",
        "@modelcontextprotocol/server-filesystem",
        "/Users/yourname/projects/ai-agent-workspace"
      ]
    }
  }
}

Key Notes

• The filesystem server is executed using npx, so no separate installation is required.
• The last argument specifies the root directory Claude is allowed to access.
• All file operations performed by the AI agent are restricted to this directory.

✅ Step 3: Verify Filesystem MCP Is Configured with Claude

After saving the configuration:

1. Restart Claude Desktop (if it’s already running).
2. Open a new conversation.
3. Enter a simple prompt such as:

List the files available in the configured workspace directory.

List the files available in the configured workspace directory.

If the configuration is correct, Claude will successfully interact with the file system through MCP.

This step confirms that Claude is now connected to the Filesystem MCP Server and ready to perform controlled file system operations.

Real-World Testing Scenario: Dynamic Test Data Generation for a POST API

You are testing a POST API that creates customer records.
Each test run requires different request bodies based on the test scenario. Manually crafting and managing these request payloads quickly becomes error-prone and time-consuming.

This is where an AI agent with filesystem access via MCP becomes extremely useful.

Example: Generating Dynamic POST API Test Data from Swagger Using MCP

Let’s look at a practical testing example using the Swagger Petstore API.

Assume we want to test the POST /pet endpoint. Instead of manually creating request bodies, we’ll use an AI agent with Filesystem MCP access to generate test data directly from the API contract.

Test Objective

• Read the Swagger contract (petStore.json)
• Generate 10 valid request bodies for the POST /pet endpoint
• Store them in a CSV (Excel-compatible) file
• Each row represents a complete JSON request body

This excel file can then be used in data-driven API tests.

Test Setup

• petStore.json (Swagger/OpenAPI contract) is already available in the MCP workspace directory
• Filesystem MCP Server is configured with Claude
• The AI agent has read access to the Swagger file and write access to the test data directory

Sample Prompt to the AI Agent

Read the petStore.json Swagger contract.

Identify the request schema for the POST /pet endpoint.

Create a CSV file containing 10 sample request bodies based on the schema.
Each row should represent a valid JSON request body.

Save the file as pet_post_requests.csv.

Read the petStore.json Swagger contract.

Identify the request schema for the POST /pet endpoint.

Create a CSV file containing 10 sample request bodies based on the schema.
Each row should represent a valid JSON request body.

Save the file as pet_post_requests.csv.