MCP Workflows

Learn how to effectively use Spotlight MCP tools with your AI coding assistant for common debugging scenarios.

Quick Start Workflow

The simplest way to start debugging with Spotlight MCP:

Your prompt:

"Check if there are any errors in my application"

What the AI does:

Calls search_errors with recent timeWindow
Analyzes any errors found
Suggests fixes or asks for more context

This works because the AI assistant knows to check Spotlight first when you mention errors or issues.

Common Workflows

1. Error Investigation

Scenario: User reports an error or unexpected behavior.

Step 1: Find the Error

Your prompt:

"My login page is broken. What's the error?"

AI action:

search_errors({
  filters: { timeWindow: 300 }
})

Step 2: Analyze the Error

The AI receives:

TypeError: Cannot read property 'token' of null
  at handleLogin (auth.js:42)
  at onClick (LoginButton.tsx:18)

AI response:

I found a TypeError in auth.js at line 42. The error indicates
that you're trying to access the 'token' property on a null object.

Step 3: Deep Dive with Code

Your follow-up:

"Show me the code around that error"

AI action:

Reads auth.js lines 35-50
Analyzes the code context
Suggests fix

Your follow-up:

"Is there a trace for this request?"

AI action:

search_traces({
  filters: { timeWindow: 60 }
})

Then gets trace details with get_traces if trace ID found.

2. Performance Investigation

Scenario: Application feels slow, need to identify bottlenecks.

Step 1: List Recent Traces

Your prompt:

"Why is my API so slow?"

AI action:

search_traces({
  filters: { timeWindow: 300 }
})

Step 2: Identify Slow Traces

AI response:

I found several traces. Here are the slowest:
- GET /api/users/profile: 2,450ms
- POST /api/data/upload: 1,820ms
- GET /api/products/search: 980ms

The profile endpoint is notably slow. Let me analyze it.

Step 3: Analyze Slow Trace

AI action:

get_traces({
  traceId: "abc12345"
})

AI response:

The slow profile request breaks down as:
- Database query: 2,100ms (bottleneck!)
- JSON processing: 200ms
- Response formatting: 150ms

The database query is taking most of the time.
Would you like me to look at the query?

Step 4: Fix and Verify

After making changes:

Your prompt:

"Check if the profile endpoint is faster now"

AI checks recent traces again and compares durations

3. Understanding Application Behavior

Scenario: Need to understand how a feature works.

Step 1: Trigger the Feature

Interact with your application
Trigger the feature you want to understand

Step 2: Check Logs

Your prompt:

"What happened when I clicked the submit button?"

AI action:

search_logs({
  filters: { timeWindow: 60 }
})

Step 3: Analyze Flow

AI response:

I can see the following flow:
1. Form validation started
2. API request to /api/submit
3. Database insert operation (25ms)
4. Email notification triggered
5. Success response returned

Everything looks normal. The operation took 180ms total.

4. Debugging Specific Files

Scenario: You made changes to a file and want to check for issues.

Step 1: Check for Errors

Your prompt:

"Are there any errors in auth.tsx?"

AI action:

search_errors({
  filters: { filename: "auth.tsx" }
})

Step 2: Check Logs

Your prompt:

"Show me logs from auth.tsx"

AI action:

search_logs({
  filters: { filename: "auth.tsx" }
})

Step 3: Analyze Together

The AI can now correlate errors and logs from the same file to give you a complete picture.

5. Regression Testing

Scenario: After deployment or changes, verify nothing broke.

Step 1: Baseline Check

Your prompt:

"Check for any errors or issues after my deployment"

AI action:

search_errors({
  filters: { timeWindow: 300 }
})

Step 2: Performance Comparison

Your prompt:

"Compare performance before and after"

AI can:

Get recent traces
Calculate average durations
Identify regressions

6. Distributed Tracing Analysis

Scenario: Debugging a request that spans multiple services.

Step 1: Find the Request Trace

Your prompt:

"I made a request to /api/orders/create. Show me the full trace."

AI action:

search_traces({
  filters: { timeWindow: 60 }
})

Filters for transactions matching the path.

Step 2: Analyze the Span Tree

AI action:

get_traces({
  traceId: "found-trace-id"
})

AI response:

Transaction: POST /api/orders/create (450ms)
├─ Database: Check inventory (45ms)
├─ HTTP: Call payment service (280ms)
│  ├─ Payment validation (50ms)
│  └─ Charge card (220ms)
├─ Database: Create order (80ms)
└─ HTTP: Send notification (45ms)

The payment service is the bottleneck at 280ms.

Advanced Patterns

Correlating Errors with Traces

When an error includes a trace ID:

Your prompt:

"This error mentions trace xyz123. What was happening?"

AI action:

Get trace details: get_traces({ traceId: "xyz123" })
Analyze span tree
Identify where error occurred in the flow

Time-Based Analysis

Investigate issues during a specific time:

Your prompt:

"What happened around 2:30 PM when the app crashed?"

AI action:

Searches errors around that timeWindow
Gets related traces
Analyzes logs from that period
Correlates all events to build timeline

Pattern Detection

Find recurring issues:

Your prompt:

"Are there any recurring errors?"

AI action:

Gets all recent errors
Groups by error message/type
Identifies patterns
Reports frequency and commonalities

Effective Prompts

Good Prompts

These prompts work well with Spotlight MCP:

✅ “Are there any errors?”
✅ “Show me errors from auth.js”
✅ “Why is the API slow?”
✅ “What happened in the last 5 minutes?”
✅ “Analyze trace abc123”
✅ “Show me database query logs”

Prompts That Need Context

These might need clarification:

⚠️ “Fix the bug” → Be specific about what’s broken
⚠️ “It’s slow” → Specify what action or endpoint
⚠️ “Something’s wrong” → Describe the symptom

Following Up

Always effective:

“Show me the code”
“What’s causing this?”
“How can I fix it?”
“Are there related errors?”
“Check the trace for this request”

Best Practices

1. Start Broad, Then Narrow

You: "Are there any errors?"
AI: [Finds multiple errors]

You: "Focus on errors in the auth module"
AI: [Filters to auth-related files]

You: "Show me the code for that error"
AI: [Reads specific file and analyzes]

2. Use Timeframes Effectively

Last 60s - Immediate issues you just triggered
Last 5min - Recent session activity
Last hour - Broader investigation

3. Combine Tools

The AI can use multiple tools together:

You: "Debug the slow checkout process"

AI workflow:
1. search_traces → Find checkout traces
2. get_traces → Analyze slow trace
3. search_logs → Check related logs
4. search_errors → Look for errors

4. Reproduce First

For best results:

Trigger the issue in your app
Then ask the AI to investigate
Fresh data gives better insights

5. Iterate

Don’t expect one prompt to solve everything:

You: "Why did this fail?"
AI: [Finds error]

You: "What was the request data?"
AI: [Shows context from error]

You: "Is this validation working correctly?"
AI: [Reads validation code]

You: "How should I fix it?"
AI: [Suggests solution]

Troubleshooting Workflows

”No Errors Found”

What to do:

Reproduce the issue in your app
Ask AI to check again immediately
Make sure your app is running with Spotlight enabled

”No Traces Available”

What to do:

Verify Sentry SDK has tracing enabled
Trigger some requests in your app
Check SDK configuration with AI

”Can’t Find Specific Event”

What to do:

Widen the time window
Remove file filters
Ask AI to show everything

Example Debugging Session

Here’s a complete example:

You: "My app crashed when I clicked submit. What happened?"

AI: Let me check for errors...
[Calls search_errors]

AI: I found a TypeError in forms/SubmitButton.tsx at line 34:
     "Cannot read property 'validate' of undefined"

     Would you like me to analyze the code?

You: "Yes, and show me what happened before the crash"

AI: [Reads SubmitButton.tsx code]
[Calls search_logs for recent activity]

AI: Here's what I found:

     Code issue: Line 34 tries to call this.validator.validate(),
     but this.validator is undefined.

     Logs show:
     1. Form render started
     2. Submit button clicked
     3. Crash occurred - no validator was initialized

     The validator is supposed to be set in the constructor,
     but it looks like it's only set when props.validationSchema
     exists. In your case, that prop might be missing.

     Would you like me to suggest a fix?

You: "Yes please"

AI: [Suggests adding null check or default validator]

Next Steps

Learn about MCP tools - Detailed tool reference
MCP setup guide - Getting started
CLI workflows - Using Spotlight from terminal

MCP Workflows

Quick Start Workflow

Common Workflows

1. Error Investigation

Step 1: Find the Error

Step 2: Analyze the Error

Step 3: Deep Dive with Code

Step 4: Check Related Traces

2. Performance Investigation

Step 1: List Recent Traces

Step 2: Identify Slow Traces

Step 3: Analyze Slow Trace

Step 4: Fix and Verify

3. Understanding Application Behavior

Step 1: Trigger the Feature

Step 2: Check Logs

Step 3: Analyze Flow

4. Debugging Specific Files

Step 1: Check for Errors

Step 2: Check Logs

Step 3: Analyze Together

5. Regression Testing

Step 1: Baseline Check

Step 2: Performance Comparison

6. Distributed Tracing Analysis

Step 1: Find the Request Trace

Step 2: Analyze the Span Tree

Advanced Patterns

Correlating Errors with Traces

Time-Based Analysis

Pattern Detection

Effective Prompts

Good Prompts

Prompts That Need Context

Following Up

Best Practices

1. Start Broad, Then Narrow

2. Use Timeframes Effectively

3. Combine Tools

4. Reproduce First

5. Iterate

Troubleshooting Workflows

”No Errors Found”

”No Traces Available”

”Can’t Find Specific Event”

Example Debugging Session

Next Steps