⚙️ Analyze OpenObserve logs and traces with an AI MCP observability toolkit

⚡ 78 views · ⚙️ DevOps & CI/CD

Description

🔎 AI Observability Toolkit for OpenObserve MCP Server (Logs + Traces)

An MCP server that exposes 10 specialized AI tools for deep observability over your OpenObserve logs and traces.

Designed for AI agents to perform:

• Schema inspection
• Error fingerprinting
• Traffic anomaly detection
• Latency profiling
• Dependency bottleneck detection
• Precise trace forensics

This server transforms OpenObserve into a structured, AI-queryable observability engine.

🧠 Overview

Instead of giving an AI raw log access, this MCP server provides purpose-built forensic tools.

The AI can:

• Inspect available fields before writing queries
• Detect real root causes instead of duplicate errors
• Identify traffic spikes
• Profile p99 latency per operation
• Detect cold starts
• Identify slow dependencies
• Map exactly which span failed inside a trace
• Run flexible SQL queries on demand

🛠️ Exposed MCP Tools

1️⃣ Stream Schema Inspection

Purpose:
Allows the AI to see available fields before constructing queries.

What it does:

• Executes DESCRIBE default
• Prevents hallucinated fields
• Enables safe query generation

2️⃣ Unique Error Fingerprinting

Purpose:
Groups identical error messages to reveal true root causes.

What it does:

• Groups by message
• Counts occurrences
• Returns top recurring failures

3️⃣ Volume Trend Analysis

Purpose:
Detects sudden spikes in log volume.

What it does:

• 1-minute histogram over _timestamp
• Surfaces abnormal traffic bursts
• Useful for detecting DDoS or recursive loops

4️⃣ Log Pattern Discovery

Purpose:
Summarizes common log prefixes to understand normal behavior.

What it does:

• Groups by first 20 characters of message
• Helps anomaly detection
• Builds behavioral baseline

5️⃣ P99 Latency Analysis (Traces)

Purpose:
Identifies the slowest 1% of operations.

What it does:

• Uses approx_percentile_cont(duration, 0.99)
• Groups by operation_name
• Surfaces performance outliers

6️⃣ Cold-Start Identification (Traces)

Purpose:
Detects slow initialization spans.

What it does:

• Filters operation_name = 'init'
• Identifies unusually long startup spans
• Useful for serverless and containerized systems

7️⃣ Dependency Hotspots (Traces)

Purpose:
Finds which external service causes the most delay.

What it does:

• Groups by service_name
• Calculates average duration
• Orders by slowest dependency

8️⃣ SQL Logs Query

Purpose:
Flexible SQL execution for logs.

What it does:

• Accepts full SQL query
• Time-bounded search
• Supports root cause analysis, security auditing, performance debugging

9️⃣ Span Error Mapping (Traces)

Purpose:
Pinpoints exactly which span failed inside a trace.

What it does:

• Filters spans where status_code >= 400
• Requires trace_id
• Returns span_id and operation_name

🔟 SQL Traces Query

Purpose:
Flexible SQL execution for trace data.

What it does:

• Accepts full SQL query
• Time-bounded search
• Enables advanced trace-level investigations

⚙️ Architecture Notes

• All tools are exposed via MCP Server Trigger
• Connected using ai_tool bindings
• Queries are time-bounded using start_time and end_time
• Uses HTTP Basic Auth for OpenObserve
• Logs endpoint: /api/default/_search
• Traces endpoint: /api/default/_search?type=traces

🚀 Use Cases

• Why did my API slow down in the last hour?
• What are the most common errors today?
• Which service dependency is causing latency?
• Show me where this trace failed.
• Is there abnormal traffic right now?

The AI can now answer all of these using your real telemetry.

🔗 Nodes Used

MCP Server Trigger

📥 Import

Download workflow.json and import into n8n: Workflow menu → Import from File

📖 Importing guide · 🔑 Credential setup