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Debugging Your MCP Server

While developing your MCP server, it’s essential to ensure it’s doing what it’s intended to do. This guide covers the best tools and methods for testing and debugging your MCP servers.

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Why Debug Your MCP Server?

Before deploying your MCP server, thorough testing helps you:
  • Verify all tools and resources work as expected
  • Catch configuration errors early
  • Ensure AI agents can properly interact with your server
  • Identify performance issues and bottlenecks
Best for: Initial development and comprehensive testing We’ve created an open-source platform specifically designed for testing and debugging MCP servers.

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Get Started with MCP Playground

  1. Repository: https://github.com/rosaboyle/mcp-playground
  2. Installation: Clone the repository and follow the setup instructions
  3. Connect: Add your MCP server URL to the playground
  4. Test: Interactive interface to test all your MCP tools and resources

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Features

Interactive Testing

Test tools and resources with real-time feedback

Request/Response Viewer

See exactly what data is being sent and received

Error Detection

Detailed error messages and debugging information

Performance Monitoring

Track response times and identify bottlenecks

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How to Use

  1. Install the playground from the GitHub repository
  2. Start your MCP server (locally or deployed)
  3. Add your MCP server URL to the playground
  4. Test individual tools and verify responses
  5. Check resources to ensure data is accessible
  6. Debug any issues using the detailed logs

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Method 2: Postman Testing

Best for: API-level testing and automation Postman provides excellent support for testing MCP servers through its standard HTTP request features.

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Setup Instructions

  1. Open Postman and create a new collection
  2. Add your MCP server URL as the base URL
  3. Configure headers as required by your MCP server
  4. Create requests for each tool and resource

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Testing Workflow

// Example: Testing a tool
POST {{mcp_server_url}}/tools/execute
Content-Type: application/json

{
  "tool_name": "your_tool_name",
  "arguments": {
    "param1": "value1",
    "param2": "value2"
  }
}

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Benefits

  • Automated testing with collection runner
  • Environment variables for different server instances
  • Test scripts for validation
  • Easy sharing with team members

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Method 3: MCP Inspector

Best for: Protocol-level debugging and validation Anthropic provides an official MCP Inspector tool for testing MCP server compliance and functionality.

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About MCP Inspector

The MCP Inspector helps you:
  • Validate MCP protocol compliance
  • Test server initialization
  • Verify tool and resource definitions
  • Check error handling

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How to Access

MCP Inspector is available through Anthropic’s official tools. Check the Anthropic documentation for the latest access instructions and installation guide.

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Usage Instructions

  1. Install the MCP Inspector following Anthropic’s documentation
  2. Configure your MCP server connection
  3. Run diagnostics to check protocol compliance
  4. Review results and fix any identified issues
  5. Re-test until all checks pass

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What It Checks

  • MCP protocol version compatibility
  • Tool and resource schema validation
  • Error response formatting
  • Connection stability
  • Performance characteristics

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Method 4: LLM Client Testing

Best for: End-to-end user experience testing
This method is not recommended for early debugging. Use it only after your MCP server passes testing with the above methods.

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When to Use This Method

Only test with LLM clients when:
  • ✅ Your MCP server passes all playground tests
  • ✅ Postman testing shows consistent responses
  • ✅ MCP Inspector validates protocol compliance
  • ✅ You’re ready to test real user scenarios
Test your MCP server with popular AI applications:

Cursor

AI-powered code editor

Windsurf

AI development environment

Claude Desktop

Anthropic’s official client

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Testing Process

  1. Connect your MCP server to the LLM client
  2. Create test prompts that should trigger your tools
  3. Monitor behavior - how does the AI use your tools?
  4. Check responses - are they what you expected?
  5. Identify confusion points - where does the AI struggle?

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Example Test Prompts

// For a file management MCP
"Please list all files in the current directory and then create a new file called 'test.txt' with the content 'Hello World'"

// For a database MCP  
"Show me all users in the database, then add a new user with name 'John Doe' and email 'john@example.com'"

// For a task management MCP
"Create a new task called 'Review documentation' with priority 'high', then show me all pending tasks"

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Best Practices

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Development Workflow

1

Start with MCP Playground

Test basic functionality and fix obvious issues
2

Use Postman for Edge Cases

Test error conditions and boundary cases
3

Validate with MCP Inspector

Ensure protocol compliance
4

Deploy and Test

Test the deployed version before LLM integration
5

LLM Integration Testing

Final validation with real AI clients

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Common Issues and Solutions

Symptoms: Tools appear in the client but don’t executeSolutions:
  • Check tool schema definitions
  • Verify parameter validation
  • Review error logs for execution failures
  • Test with MCP Playground first
Symptoms: AI can’t access your data resourcesSolutions:
  • Verify resource URIs are correct
  • Check permissions and authentication
  • Test resource endpoints individually
  • Review MCP protocol compliance
Symptoms: Long delays between requests and responsesSolutions:
  • Profile your tool execution times
  • Optimize database queries
  • Add caching where appropriate
  • Check network connectivity
Symptoms: AI calls tools incorrectly or unnecessarilySolutions:
  • Improve tool descriptions and examples
  • Add better parameter validation
  • Review tool naming conventions
  • Test with clearer prompts

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Debugging Checklist

Before considering your MCP server production-ready:
  • Basic functionality tested in MCP Playground
  • All tools execute successfully with valid inputs
  • All resources return expected data
  • Error handling works for invalid inputs
  • Protocol compliance validated with MCP Inspector
  • Performance meets your requirements
  • LLM integration tested with target clients
  • Documentation updated with any changes

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Next Steps

Deploy Your Server

Ready to deploy? Follow our deployment guide

Best Practices

Learn how to build better MCP servers

GitHub Integration

Set up CI/CD for your MCP server

Get Help

Need assistance? Contact our support team

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Additional Resources