FIS Recommender MCP Server

Local setup required. This server has to be cloned and prepared on your machine before you register it in Claude Code.
1

Set the server up locally

Run this once to clone and prepare the server before adding it to Claude Code.

Run in terminal
pip install bedrock-agentcore-starter-toolkit
2

Register it in Claude Code

After the local setup is done, run this command to point Claude Code at the built server.

Run in terminal
claude mcp add -e "AWS_REGION=${AWS_REGION}" fis-recommender -- python3 "<FULL_PATH_TO_FIS_RECOMMENDER_MCP>/dist/index.js"

Replace <FULL_PATH_TO_FIS_RECOMMENDER_MCP>/dist/index.js with the actual folder you prepared in step 1.

Required:AWS_REGION
README.md

Automatically recommend and generate AWS FIS experiment templates

FIS Recommender MCP Server

An MCP (Model Context Protocol) server that automatically recommends AWS Fault Injection Simulator (FIS) experiments based on DevOps Agent findings. Helps teams quickly design chaos engineering experiments to validate system resilience.

Features

  • 🔍 Analyzes DevOps findings and suggests relevant FIS experiments
  • 🎯 Maps issues to appropriate fault injection actions
  • 📋 Generates complete FIS experiment templates
  • ⚡ Integrates seamlessly with Kiro CLI and other MCP clients

Deployment Options

Option 1: MCP Server (for DevOps Agent)

Deploy MCP Server
# Install toolkit
pip install bedrock-agentcore-starter-toolkit

# Configure and deploy
agentcore configure -e server.py --protocol MCP
agentcore launch
Setup OAuth (Cognito)
chmod +x setup_cognito.sh
source setup_cognito.sh

Save the output (Client ID, Discovery URL, Bearer Token).

Register in DevOps Agent Console
  1. Go to AIDevOps → Your Agent → Settings → MCP Servers
  2. Add Server:
    • Endpoint: https://bedrock-agentcore.{REGION}.amazonaws.com/runtimes/{ENCODED_ARN}/invocations
    • Client ID: From step 2
    • Exchange URL: https://{COGNITO_DOMAIN}.auth.{REGION}.amazoncognito.com/oauth2/token
    • Authorization URL: https://{COGNITO_DOMAIN}.auth.{REGION}.amazoncognito.com/oauth2/authorize
    • Scopes: openid
    • PKCE: Enabled
Test

Ask DevOps Agent:

"Recommend FIS experiments for network latency issues"

See Setup Guide for complete deployment instructions.

Option 2: Lambda Client (for API Gateway, EventBridge)

chmod +x deploy_lambda.sh
./deploy_lambda.sh

# Test
aws lambda invoke --function-name fis-recommender-mcp-client --region {REGION} \
  --payload '{"tool":"recommend_fis_experiments","arguments":{"finding":{"summary":"network latency"}}}' \
  response.json && cat response.json

See Lambda Deploy Guide for detailed instructions.

Local Development

Clone the Repository

git clone https://github.com/pimisael/fis-recommender-mcp.git
cd fis-recommender-mcp
chmod +x server.py

Configure MCP Client

For Kiro CLI

Add to ~/.kiro/mcp-servers.json:

{
  "mcpServers": {
    "fis-recommender": {
      "command": "python3",
      "args": ["/absolute/path/to/fis-recommender-mcp/server.py"],
      "env": {
        "AWS_REGION": "us-east-1"
      }
    }
  }
}
For Claude Desktop

Add to ~/Library/Application Support/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "fis-recommender": {
      "command": "python3",
      "args": ["/absolute/path/to/fis-recommender-mcp/server.py"],
      "env": {
        "AWS_REGION": "us-east-1"
      }
    }
  }
}

Usage Examples

Example 1: Network Latency Issue

Prompt:

I have a DevOps finding about network latency causing timeouts in my application. 
Can you recommend FIS experiments to test this?

Finding details:
- ID: finding-001
- Summary: "High network latency between services causing request timeouts"
- Type: NETWORK_ISSUE

Response: The MCP server will recommend:

  • Action: aws:network:disrupt-connectivity
  • Duration: 10 minutes
  • Target: Network interfaces
  • Stop condition: CloudWatch alarm on error rate

Example 2: Database Availability

Prompt:

Recommend FIS experiments for this finding:
{
  "id": "finding-db-001",
  "summary": "Database connection failures during peak load",
  "type": "DATABASE_ISSUE"
}

Response:

  • Action: aws:rds:reboot-db-instances
  • Duration: 2 minutes
  • Target: RDS instances
  • Tests application's database failover handling

Example 3: CPU Stress Testing

Prompt:

We had a CPU spike incident. Generate a FIS template to test our auto-scaling.

Finding: "CPU utilization reached 95% causing service degradation"

Response: Complete FIS experiment template with:

  • EC2 instance stop action
  • 3-minute duration
  • CloudWatch alarm stop condition
  • Target selection by tags

Example 4: Memory Pressure

Prompt:

Create FIS experiments to validate our memory monitoring:
- Finding ID: mem-leak-001
- Issue: Memory leak caused OOM errors
- Need to test alerting and recovery

Response:

  • Action: aws:ssm:send-command (memory stress)
  • Duration: 5 minutes
  • SSM document for memory consumption
  • Tests monitoring and auto-recovery

Standalone Testing

Run the example script to test without an MCP client:

python3 example.py

This will analyze sample findings and display recommendations.

Supported Finding Types

Network & Connectivity

Finding Keyword FIS Action Duration Use Case
network aws:network:disrupt-connectivity 5 min Test network partition handling
latency aws:network:disrupt-connectivity 10 min Validate timeout configurations
packet loss aws:ecs:task-netwo

Tools (1)

recommend_fis_experimentsAnalyzes DevOps findings to suggest relevant AWS FIS experiments and generate templates.

Environment Variables

AWS_REGIONrequiredThe AWS region where the FIS experiments will be managed.

Configuration

claude_desktop_config.json
{"mcpServers": {"fis-recommender": {"command": "python3", "args": ["/absolute/path/to/fis-recommender-mcp/server.py"], "env": {"AWS_REGION": "us-east-1"}}}}

Try it

Recommend FIS experiments for network latency issues causing request timeouts.
Generate a FIS template to test our auto-scaling based on a CPU utilization spike of 95%.
Recommend FIS experiments for database connection failures during peak load.
Create FIS experiments to validate our memory monitoring after an OOM error incident.

Frequently Asked Questions

What are the key features of FIS Recommender?

Analyzes DevOps findings to suggest relevant FIS experiments. Maps infrastructure issues to appropriate fault injection actions. Generates complete FIS experiment templates. Integrates with Kiro CLI and Claude Desktop.

What can I use FIS Recommender for?

Validating system resilience against network partition and latency issues. Testing application database failover handling during peak load. Verifying auto-scaling behavior under high CPU stress. Validating memory monitoring and auto-recovery procedures.

How do I install FIS Recommender?

Install FIS Recommender by running: pip install bedrock-agentcore-starter-toolkit

What MCP clients work with FIS Recommender?

FIS Recommender works with any MCP-compatible client including Claude Desktop, Claude Code, Cursor, and other editors with MCP support.

Turn this server into reusable context

Keep FIS Recommender docs, env vars, and workflow notes in Conare so your agent carries them across sessions.

Need the old visual installer? Open Conare IDE.
Open Conare