use_aws_mcp + Klavis AI: Complete MCP Integration

use_aws_mcp is a MCP server that amazon-q-cli's use_aws tool extracted into independent mcp, for general aws api usage..

When integrated with Klavis AI, you can:

• Access use_aws_mcp functionality through Klavis AI

This guide provides step-by-step instructions to set up use_aws_mcp in Klavis AI, including configuration, examples, and troubleshooting.

What You'll Achieve

After completing this setup:

• use_aws_mcp will be fully integrated and operational
• You can use use_aws_mcp tools directly in Klavis AI
• All use_aws_mcp capabilities will be available for your workflows

Prerequisites

Before starting, ensure you have:

• Klavis AI installed and configured
• Compatible operating system (Cloud (klavis.ai), Self-Hosted (Docker), Python SDK, TypeScript SDK)

Installation

Step 1: Install use_aws_mcp

Configuration

Step 2: Configure Klavis AI

1. Open Klavis AI settings
2. Navigate to MCP server configuration
3. Add use_aws_mcp server with appropriate settings
4. Save and restart if needed

Examples

Once configured, you can use use_aws_mcp in Klavis AI:

Testing Your Setup

1. Launch Klavis AI
2. Verify use_aws_mcp is available in the tools list
3. Test basic use_aws_mcp functionality

Troubleshooting

Common Issues

API Authentication Failed

Symptoms: 401 Unauthorized errors, Invalid API key message

Cause: Missing or incorrect API key

Solution:

• Verify API key is correctly copied from klavis.ai dashboard
• Check API key is set in environment variable or code
• Ensure API key has not expired
• Regenerate API key if necessary

OAuth Authorization Errors

Symptoms: OAuth redirect failures, Authentication loop, Token refresh errors

Cause: OAuth credentials not configured or expired

Solution:

• Complete OAuth authorization flow for required services
• Check redirect URLs are correctly configured
• Verify OAuth app credentials in service provider settings
• Contact Klavis support for OAuth troubleshooting

Tool Call Failures

Symptoms: Tool not found errors, Execution timeouts, Rate limit errors

Cause: Tool not properly configured or service rate limits exceeded

Solution:

• Verify tool name matches available tools list
• Check service-specific rate limits and quotas
• Ensure required parameters are provided correctly
• Review Klavis API logs for detailed error messages

Context Window Overload

Symptoms: Too many tools error, Context length exceeded, Slow responses

Cause: Too many tools exposed to AI agent at once

Solution:

• Use Strata Progressive Discovery to expose tools hierarchically
• Reduce number of simultaneous MCP servers in Strata configuration
• Filter tools based on current task context
• Enable intelligent tool filtering in Strata settings

Docker Deployment Issues

Symptoms: Container fails to start, Port conflicts, Connection refused

Cause: Docker configuration or port binding errors

Solution:

• Ensure Docker is installed and running
• Check port 5000 is not already in use (or change port mapping)
• Verify Docker image pulled successfully
• Check container logs: docker logs <container-id>

use_aws_mcp not appearing in Klavis AI

Symptoms: Server not listed, Tools not available

Cause: Configuration or installation issue

Solution:

• Verify configuration syntax
• Check use_aws_mcp installation
• Restart Klavis AI
• Check logs for error messages

Next Steps

Now that use_aws_mcp is integrated with Klavis AI:

• Explore all use_aws_mcp capabilities through Klavis AI
• Check out other MCP servers that work with Klavis AI
• Join the MCP community for tips and support
• Consider contributing to use_aws_mcp development

Need Help?

• Search for use_aws_mcp documentation
• Check the Klavis AI MCP guide
• Join the MCP community discussions