pip install uv

> q  
✓ opensearch_mcp_server loaded in 0.36 s
✓ 1 of 1 mcp servers initialized.

> /tools

𝗧𝗼𝗼𝗹 𝗣𝗲𝗿𝗺𝗶𝘀𝘀𝗶𝗼𝗻
▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔▔
𝗕𝘂𝗶𝗹𝘁-𝗶𝗻:
- execute_bash * trust read-only commands
- fs_read  * 𝘁𝗿𝘂𝘀𝘁𝗲𝗱
- fs_write * not trusted
- report_issue * 𝘁𝗿𝘂𝘀𝘁𝗲𝗱
- use_aws  * trust read-only commands

𝗼𝗽𝗲𝗻𝘀𝗲𝗮𝗿𝗰𝗵_𝗺𝗰𝗽_𝘀𝗲𝗿𝘃𝗲𝗿 (𝗠𝗖𝗣):
- opensearch_mcp_server___GetShardsTool  * not trusted
- opensearch_mcp_server___IndexMappingTool * not trusted
- opensearch_mcp_server___ListIndexTool  * not trusted
- opensearch_mcp_server___SearchIndexTool  * not trusted

> list indices
I'll help you list the indices in OpenSearch. Let me retrieve that information 
for you.

🛠️ Using tool: ListIndexTool from mcp server opensearch_mcp_server
 ⋮ 
 ● Running ListIndexTool with the param:
 ⋮ {
 ⋮ "arguments": {},
 ⋮ "name": "ListIndexTool"
 ⋮ }
Allow this action? Use 't' to trust (always allow) this tool for the session. [y/n/t]:

> y
 ⋮ 
 ● Completed in 1.8s
Here are the indices in your OpenSearch cluster:
1. .plugins-ml-model-group
2. tmdb
3. .plugins-ml-memory-meta
4. .ql-datasources
5. .plugins-ml-task
...

> give me a quick summary of my eCommerce data
I'll provide a summary of your eCommerce data. Let me query the OpenSearch eCommerce sample data 
index to get that information.

🛠️ Using tool: SearchIndexTool from mcp server opensearch_mcp_server
...

# eCommerce Data Summary
Here's a quick summary of your eCommerce sample data:

## Order Statistics
• Total orders: 4,675
• Average order price: $75.06
• Minimum order price: $6.99
• Maximum order price: $2,250.00

## Top Categories
1. Men's Clothing (2,024 orders)
2. Women's Clothing (1,903 orders)
3. Women's Shoes (1,136 orders)
4. Men's Shoes (944 orders)
5. Women's Accessories (830 orders)

## Top Manufacturers
1. Low Tide Media (1,553 orders)
2. Elitelligence (1,370 orders)
3. Oceanavigations (1,218 orders)
4. Tigress Enterprises (1,055 orders)
5. Pyramidustries (947 orders)

## Customer Demographics
• Female customers: 2,433 orders (52%)
• Male customers: 2,242 orders (48%)

This data appears to be a sample dataset with a focus on clothing and accessories, with a fairly 
even gender distribution among customers.

> check my otel traces, show me latency by service name

I'll help you analyze the OpenTelemetry (OTel) traces in your system to show latency by service 
name. Let me query the relevant index.

🛠️ Using tool: SearchIndexTool from mcp server opensearch_mcp_server
 ⋮ 
 ● Running SearchIndexTool with the param:
...

Based on your OpenTelemetry traces, here's a breakdown of latency by service name:

## Service Latency Summary (in nanoseconds)
| Service | Requests | Min Latency | Avg Latency | Max Latency | Avg (ms) |
|---------|----------|------------|-------------|-------------|----------|
| frontend | 4,486 | 5,376 | 4,212,749 | 297,229,056 | 4.21 ms |
| cartservice | 1,151 | 573,500 | 908,025 | 5,473,600 | 0.91 ms |
| productcatalogservice | 976 | 5,025 | 1,179,214 | 9,671,364 | 1.18 ms |
| loadgenerator | 792 | 4,075,763 | 12,225,651 | 298,967,030 | 12.23 ms |
| checkoutservice | 662 | 258,431 | 6,623,067 | 288,126,051 | 6.62 ms |
| paymentservice | 403 | 8,448 | 108,115 | 4,312,064 | 0.11 ms |
| featureflagservice | 385 | 468,824 | 786,792 | 4,978,760 | 0.79 ms |
| recommendationservice | 271 | 880,730 | 8,177,191 | 13,767,667 | 8.18 ms |
| emailservice | 196 | 63,342 | 3,739,964 | 189,894,340 | 3.74 ms |
| adservice | 170 | 36,500 | 3,126,550 | 141,348,925 | 3.13 ms |

## Key Observations:
1. Highest Traffic Services:
 • frontend (4,486 requests)
 • cartservice (1,151 requests)
 • productcatalogservice (976 requests)

2. Highest Average Latency:
 • loadgenerator (12.23 ms)
 • recommendationservice (8.18 ms)
 • checkoutservice (6.62 ms)

3. Highest Maximum Latency (Potential Outliers):
 • loadgenerator (298.97 ms)
 • frontend (297.23 ms)
 • checkoutservice (288.13 ms)

4. Most Efficient Services:
 • paymentservice (0.11 ms avg)
 • featureflagservice (0.79 ms avg)
 • cartservice (0.91 ms avg)

The data shows that while the frontend handles the most requests, the loadgenerator, 
recommendationservice, and checkoutservice have the highest average latencies. The paymentservice 
appears to be the most efficient with the lowest average latency.

> why does loadgenerator have high average latencies?

To understand why the loadgenerator has high average latencies, I'll need to examine more details 
about its traces. Let me query for more specific information about the loadgenerator service.

🛠️ Using tool: SearchIndexTool from mcp server opensearch_mcp_server
 ⋮ 
 ● Running SearchIndexTool with the param:
...
...

After analyzing the OpenTelemetry trace data in detail, I can explain why the loadgenerator serviceshows significantly higher latencies compared to other services in your microservices 
architecture.

**## Root Cause of High Loadgenerator Latencies**

The high latencies in the loadgenerator service are **by design** and represent the complete end-to-end 
request time, including all downstream service processing. Here's what's happening:

1. Client-Side Measurement: The loadgenerator is using Python's requests library with OpenTelemetry 
instrumentation to make HTTP calls. The spans from loadgenerator are **CLIENT spans** that measure the 
entire request lifecycle from start to finish.

2. End-to-End Timing: Looking at trace 753ef52011e443ed190dbf6f31c30cad, we can see:
 • The loadgenerator span starts at 2023-11-23T00:16:14.063978483Z
 • It ends at 2023-11-23T00:16:14.362945513Z
 • Total duration: 298.97ms

3. Nested Service Calls: The frontend service receives this request and creates its own span that 
lasts for 297.23ms, nearly the entire duration of the loadgenerator's span.

4. Cascading Service Calls: Following the trace through the system:
 • Frontend makes a gRPC call to CheckoutService (291.63ms)
 • CheckoutService processes the order (288.13ms)
 • CheckoutService makes an HTTP POST to EmailService (193.04ms)
 • And so on through multiple services

## Confirmation from Trace Analysis

When examining the complete trace 6f0611913ca16432c5952b2c76bd3f1f, we can see:
• The frontend initiates a synthetic request (linked to the loadgenerator's original request)
• This cascades through multiple services (checkout, email, etc.)
• Each service adds its own processing time
• The loadgenerator's span encompasses all of this processing time

## Why This Is Normal

1. Load Testing Tool: The loadgenerator is designed to simulate user traffic and measure complete 
response times. Its high latencies are actually a feature, not a bug.

2. Instrumentation Difference: While other services report internal processing times, the 
loadgenerator reports the complete client experience time.

3. Network Overhead: The loadgenerator spans include network latency between all services, which 
adds up significantly.

## Recommendations

1. This is expected behavior: The loadgenerator's high latencies are normal and reflect its role in 
the system.

2. Use for Performance Testing: These measurements are valuable for end-to-end performance testing.

3. Separate Metrics: When evaluating service performance, consider:
 • Internal service latencies (from each service's SERVER spans)
 • End-to-end latencies (from loadgenerator's CLIENT spans)

4. Optimization Focus: If you want to improve overall system performance, focus on the services with
the highest internal processing times (checkout, recommendation, and email services) rather than 
the loadgenerator itself.

The loadgenerator is correctly measuring the complete user experience time, which naturally 
includes all downstream service processing and network overhead.