AI-Powered Resilience Testing with Harness MCP Server and Windsurf

The complexity of modern distributed systems demands proactive resilience testing, yet the old-school chaos engineering often presents a steep learning curve that can slow adoption across teams. What if you could perform chaos experiments using simple, natural language conversations directly within your development environment?

The integration of Harness Chaos Engineering with Windsurf through the Model Context Protocol (MCP) makes this vision a reality. This powerful combination enables DevOps, QA, and SRE teams to discover, execute, and analyze chaos experiments without deep vendor-specific knowledge, accelerating your organization's journey toward building a resilience testing culture.

Simplifying Chaos Engineering

Chaos engineering has proven its value in identifying system weaknesses before they impact production. However, traditional implementations face common challenges:

Technical Complexity: Setting up experiments requires deep understanding of fault injection mechanisms, blast radius calculations, and monitoring configurations.

Learning Curve: Teams need extensive training on vendor-specific tools and chaos engineering principles before becoming productive.

Context Switching: Engineers constantly move between documentation, experiment configuration interfaces, and result analysis tools.

Skill Scaling: Organizations struggle to democratize chaos engineering beyond specialized reliability teams.

The Harness MCP integration changes this landscape by bringing chaos engineering capabilities directly into your AI-powered development workflow.

Understanding Harness Chaos Engineering MCP Tools

The Harness Chaos Engineering MCP server provides six specialized tools that cover the complete chaos engineering lifecycle:

Core Experiment Tools

chaos_experiments_list: Discover all available chaos experiments in your project. Perfect for understanding your resilience testing capabilities and finding experiments relevant to specific services.

chaos_experiment_describe: Get details about any experiment, including its purpose, target infrastructure, expected impact, and success criteria.

chaos_experiment_run: Execute chaos experiments with intelligent parameter detection and automatic configuration, removing the complexity of manual setup.

chaos_experiment_run_result: Retrieve detailed results including resilience scores, performance impact analysis, and actionable recommendations for improvement.

Advanced Monitoring Tools

chaos_probes_list: Discover all available monitoring probes that validate system health during experiments, giving you visibility into your monitoring capabilities.

chaos_probe_describe: Get detailed information about specific probes, including their validation criteria, monitoring setup, and configuration parameters.

Setting Up Harness MCP Server with Windsurf

Prerequisites

Before beginning the setup, ensure you have:

• Windsurf IDE installed 
• Harness Platform access with Chaos Engineering enabled
• Harness API key with appropriate permissions
• Go 1.23+ (to build from source)

Step 1: Build the Harness MCP Server Binary

You have multiple installation options. Choose the one that best fits your environment:

Building from Source

For advanced users who prefer building from source:

1. Clone the Repository:

‍

2. Build the Binary:

Step 2: Configure the Harness MCP Server in Windsurf

1. Navigate to your Windsurf Settings, click on Cascade, then Manage MCPs.

2. Click on View raw config to open your mcp_config.json file

‍

3. Add the below configuration to the file

‍

Step 3: Add the Path of your Binary and Harness Credentials

Gather the following information, add it to the placeholders and save the mcp_config.json file.

• Command: Path to your built harness-mcp-server binary
• API Key: Generate from your Harness account settings (Profile > My API Keys)
• Organization ID: Found in your Harness URL or organization settings
• Project ID: The project containing your chaos experiments
• Base URL: Your Harness instance URL (typically https://app.harness.io)

Step 4: Verify Installation

1. Restart Windsurf: Close and reopen Windsurf to load the new configuration
2. Go back to Mange MCPs, you should see a list of tools available

3. Test Connection: Try a simple prompt like:

"List all chaos experiments available in my project"

‍

If successful, you should see chaos-related tools with the "chaos" prefix and receive a response with your experiment list.

AI-Powered Chaos Engineering in Action

With your setup complete, let's explore how to leverage these tools effectively through natural language interactions.

‍

Discovery and Learning Phase

Service-Specific Exploration:

"I am interested in catalog service resilience. Can you tell me what chaos experiments are available?"

‍

Expected Output: Filtered list of experiments targeting your catalog service, categorized by fault type (network, compute, storage).

‍

Deep-Dive Analysis:

"Describe briefly what the pod deletion experiment does and what services it targets"

‍

Expected Output: Technical details about the experiment, including fault injection mechanism, expected impact, target selection criteria, and success metrics.

‍

Understanding Resilience Metrics:

"Describe the resilience score calculation details for the network latency experiment"

‍

Expected Output: Detailed explanation of scoring methodology, performance thresholds, and interpretation guidelines.

‍

Experiment Execution Phase

Targeted Experiment Execution:

"Can you run the pod deletion experiment on my payment service?"

‍

Expected Output: Automatic parameter detection, experiment configuration, execution initiation, and real-time monitoring setup.

Structured Overview Creation:

"Can you list the network chaos experiments and the corresponding services targeted? Tabulate if possible."

‍

Expected Output: Well-organized table showing experiment names, target services, fault types, and current status.

Monitoring Probe Discovery:

"Show me all available chaos probes and describe how they work"

‍

Expected Output: Complete catalog of available probes with their monitoring capabilities, validation criteria, and configuration details.

‍

Analysis and Reporting Phase

Result Interpretation:

"Summarise the result of the database connection timeout experiment"

‍

Expected Output: Comprehensive analysis including performance impact, resilience score, business implications, and specific recommendations for improvement.

Probe Configuration Details:

"Describe the HTTP probe used in the catalog service experiment"

‍

Expected Output: Detailed probe configuration, validation criteria, success/failure thresholds, and monitoring setup instructions.

Comprehensive Resilience Assessment:

"Scan the experiments that were run against the payment service in the last week and summarise the resilience posture for me"

‍

Expected Output: Executive-level resilience report with trend analysis, critical findings, and actionable improvement recommendations.

The Road Ahead

The convergence of AI and chaos engineering represents more than a technological advancement, it's a fundamental shift toward more accessible, and intelligent resilience testing. By embracing this approach with Harness and Windsurf, you're not just testing your systems' resilience, you're building the foundation for reliable, battle-tested applications that can withstand the unexpected challenges of production environments.

Start your AI-powered chaos engineering journey today and discover how natural language can transform the way your organization approaches system reliability.

‍