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MCP Server Demo வினவல்கள்

Kiro IDE-உடன் MCP server integration-ஐ சோதிக்க நீங்கள் பயன்படுத்தக்கூடிய எடுத்துக்காட்டு இயற்கை மொழி வினவல்களை இந்த வழிகாட்டி வழங்குகிறது.

முன்நிபந்தனைகள்​

  1. Make sure you've set up the MCP server in Kiro (see SETUP-MCP-KIRO.md)
  2. Run the multi-cloud demo to generate telemetry: python3 AI-OBS_DEMO/multi-cloud-demo.py
  3. Wait 1-2 minutes for metrics to appear in CloudWatch

Example Queries for Screenshots​

1. Token Usage Analysis​

Query: "Which model is consuming the most tokens?"

Expected Response:

{
  "token_type": "input",
  "time_range_hours": 1,
  "models": [
    {
      "model": "anthropic.claude-3-haiku-20240307-v1:0",
      "total_tokens": 475
    },
    {
      "model": "gpt-4o",
      "total_tokens": 312
    },
    {
      "model": "gemini-1.5-pro",
      "total_tokens": 289
    }
  ]
}

Alternative Queries:

  • "Show me input token usage for the last hour"
  • "How many output tokens has Claude Haiku used?"
  • "Compare token consumption across all models"

2. Latency Statistics​

Query: "What is the average latency for Claude Haiku?"

Expected Response:

{
  "model": "anthropic.claude-3-haiku-20240307-v1:0",
  "avg_latency_ms": 1234.56,
  "max_latency_ms": 1876.23,
  "min_latency_ms": 892.45,
  "time_range_hours": 1,
  "datapoints": 31
}

Alternative Queries:

  • "Show me latency statistics for all models"
  • "Which model has the highest latency?"
  • "What's the fastest model in terms of response time?"

3. Request Volume​

Query: "How many requests have been made in the last hour?"

Expected Response:

{
  "time_range_hours": 1,
  "models": [
    {
      "model": "anthropic.claude-3-sonnet-20240229-v1:0",
      "total_requests": 81
    },
    {
      "model": "anthropic.claude-3-haiku-20240307-v1:0",
      "total_requests": 31
    },
    {
      "model": "gpt-4o",
      "total_requests": 21
    }
  ]
}

Alternative Queries:

  • "Show me request counts by model"
  • "Which model is being used the most?"
  • "How many times was GPT-4o invoked?"

4. Cost Estimation​

Query: "Estimate the cost of LLM usage for the last hour"

Expected Response:

{
  "time_range_hours": 1,
  "total_estimated_cost_usd": 0.0142,
  "cost_breakdown": [
    {
      "model": "anthropic.claude-3-haiku-20240307-v1:0",
      "input_tokens": 475,
      "output_tokens": 8084,
      "estimated_cost_usd": 0.0102
    },
    {
      "model": "anthropic.claude-3-sonnet-20240229-v1:0",
      "input_tokens": 312,
      "output_tokens": 2456,
      "estimated_cost_usd": 0.0031
    }
  ],
  "note": "Costs are estimates based on Claude 3 Haiku pricing ($0.25/$1.25 per 1M tokens)"
}

Alternative Queries:

  • "What's my estimated LLM cost today?"
  • "How much am I spending on Claude models?"
  • "Calculate the cost per request"

5. Model Comparison​

Query: "Compare all models by latency and token usage"

Expected Response:

{
  "time_range_hours": 1,
  "latency": {
    "models": [
      {
        "model": "anthropic.claude-3-sonnet-20240229-v1:0",
        "avg_latency_ms": 2567.89
      },
      {
        "model": "gpt-4o",
        "avg_latency_ms": 2234.12
      },
      {
        "model": "anthropic.claude-3-haiku-20240307-v1:0",
        "avg_latency_ms": 1234.56
      }
    ]
  },
  "input_tokens": {
    "models": [
      {
        "model": "anthropic.claude-3-haiku-20240307-v1:0",
        "total_tokens": 475
      },
      {
        "model": "anthropic.claude-3-sonnet-20240229-v1:0",
        "total_tokens": 312
      }
    ]
  },
  "output_tokens": {
    "models": [
      {
        "model": "anthropic.claude-3-haiku-20240307-v1:0",
        "total_tokens": 8084
      },
      {
        "model": "anthropic.claude-3-sonnet-20240229-v1:0",
        "total_tokens": 2456
      }
    ]
  },
  "requests": {
    "models": [
      {
        "model": "anthropic.claude-3-sonnet-20240229-v1:0",
        "total_requests": 81
      },
      {
        "model": "anthropic.claude-3-haiku-20240307-v1:0",
        "total_requests": 31
      }
    ]
  }
}

Alternative Queries:

  • "Show me a comparison of all active models"
  • "Which model offers the best performance?"
  • "Compare Claude Haiku vs Claude Sonnet"

Advanced Queries​

Time Range Queries​

Query: "Show me token usage for the last 2 hours"

The MCP server supports custom time ranges using the hours parameter.

Specific Model Queries​

Query: "What's the latency for anthropic.claude-3-haiku-20240307-v1:0?"

You can query specific models using their full model IDs.

Multi-Metric Queries​

Query: "Give me a complete overview of Claude Haiku performance"

This will trigger the compare_models tool to show all metrics for the specified model.

Tips for Taking Screenshots​

Best Queries for Demo Screenshots​

  1. Cost Analysis (Most Impressive):

    "Estimate the cost of LLM usage for the last hour"

    Shows real business value with dollar amounts.

  2. Model Comparison (Most Comprehensive):

    "Compare all models by latency and token usage"

    Shows the power of unified observability across providers.

  3. Simple Query (Most Accessible):

    "Which model is consuming the most tokens?"

    Easy to understand, shows natural language capability.

Screenshot Composition Tips​

  1. Show the Query: Make sure the natural language query is visible
  2. Show the Response: Include the full JSON response with data
  3. Show Context: Include IDE context (file explorer, terminal) if possible
  4. Highlight Key Data: Point out interesting insights in the response

Example Screenshot Flow​

  1. Open Kiro IDE
  2. Open the chat panel
  3. Type: "Estimate the cost of LLM usage for the last hour"
  4. Wait for MCP server to respond
  5. Take screenshot showing:
    • Your natural language query
    • The structured JSON response
    • Cost breakdown by model
    • Total estimated cost

Troubleshooting​

"No data" Response​

Problem: MCP server returns empty results

Solutions:

  1. Run the demo to generate metrics: python3 AI-OBS_DEMO/multi-cloud-demo.py
  2. Wait 1-2 minutes for CloudWatch to ingest metrics
  3. Try increasing time range: "Show me token usage for the last 2 hours"

MCP Server Not Responding​

Problem: Queries timeout or fail

Solutions:

  1. Check MCP server is running: Look for "ai-observability" in Kiro MCP panel
  2. Verify AWS credentials: aws sts get-caller-identity
  3. Check CloudWatch permissions: Ensure read access to CloudWatch metrics
  4. Restart Kiro to reload MCP configuration

Permission Errors​

Problem: "AccessDenied" errors in responses

Solutions:

  1. Verify IAM permissions include cloudwatch:GetMetricStatistics
  2. Verify IAM permissions include cloudwatch:ListMetrics
  3. Check AWS region is set to us-east-1 in MCP config

Testing the MCP Server Directly​

You can also test the MCP server directly without Kiro:

python3 AI-OBS_DEMO/test-mcp-server.py

This will run all 5 MCP tools and display the results, useful for:

  • Verifying the MCP server works
  • Debugging issues
  • Understanding the response format
  • Generating sample data for documentation

Next Steps​

After taking screenshots:

  1. Add to Blog Post: Include screenshots in the "Demo Results" section
  2. Create Tutorial: Use screenshots to create a step-by-step guide
  3. Share with Team: Demonstrate the natural language query capability
  4. Gather Feedback: Ask developers what other queries would be useful

Additional Resources​

  • MCP Server Code: AI-OBS_DEMO/mcp-server/cloudwatch_mcp_server.py
  • Setup Guide: AI-OBS_DEMO/SETUP-MCP-KIRO.md
  • Test Script: AI-OBS_DEMO/test-mcp-server.py
  • Kiro Config: AI-OBS_DEMO/kiro-mcp-config.json