Maintain and develop an e-commerce platform that processes millions of transactions per minute, creating a wealth of telemetry data like metrics, logs, and traces across various microservices. When critical incidents occur, on-call engineers face the challenge of sifting through excessive data to uncover relevant insights, akin to finding a needle in a haystack.
This transforms observability into a source of frustration rather than insight. To mitigate this issue, I began exploring the Model Context Protocol (MCP) to add context and draw inferences from logs and traces. In this article, I’ll share my experience in creating an AI-powered observability platform, detail the system architecture, and provide actionable insights gathered along the way.
Why is observability challenging?
In modern software, observability is essential. Measuring and understanding system behavior are fundamental to reliability, performance, and user trust. As the saying goes, “What you cannot measure, you cannot improve.”
Achieving observability in today’s cloud-native, microservice architectures is more difficult than ever. A single user request might navigate dozens of microservices, each generating logs, metrics, and traces, resulting in an abundance of telemetry data:
– Tens of terabytes of logs per day
– Tens of millions of metric data points and pre-aggregates
– Millions of distributed traces
– Thousands of correlation IDs generated every minute
The challenge is not only the data volume but also its fragmentation. According to New Relic’s 2023 Observability Forecast Report, 50% of organizations report siloed telemetry data, with only 33% achieving a unified view across metrics, logs, and traces.
Logs tell part of the story, metrics another, and traces another. Without a consistent thread of context, engineers rely on manual correlation, intuition, and tedious detective work during incidents.
This complexity led me to wonder: How can AI help overcome fragmented data and offer comprehensive, useful insights? Specifically, can we make telemetry data more meaningful and accessible for both humans and machines using a structured protocol such as MCP? This central question shaped the project’s foundation.
Understanding MCP: A data pipeline perspective
Anthropic defines MCP as an open standard allowing developers to create a secure two-way connection between data sources and AI tools. This structured data pipeline includes:
– Contextual ETL for AI: Standardizing context extraction from data sources.
– Structured query interface: Allows AI queries to access transparent, understandable data layers.
– Semantic data enrichment: Embeds meaningful context into telemetry signals.
This can shift platform observability from reactive problem solving to proactive insights.
System architecture and data flow
Before implementation, let’s discuss the system architecture.
In the first layer, we develop contextual telemetry data by embedding standardized metadata into telemetry signals like traces, logs, and metrics. In the second layer, enriched data is fed into the MCP server for indexing, structuring, and providing client access to context-enriched data via APIs. Finally, an AI-driven engine uses structured telemetry data for anomaly detection, correlation, and root-cause analysis.
This layered design ensures AI and engineering teams receive context-driven, actionable telemetry insights.
Implementative deep dive: A three-layer system
Let’s explore the MCP-powered observability platform, focusing on data flows and transformations at each step.
Layer 1: Context-enriched data generation
First, ensure telemetry data contains enough context for meaningful analysis. Correlate data at creation, not analysis time.
This approach ensures every telemetry signal (logs, metrics, traces) contains core contextual data, solving correlation problems at the source.
Layer 2: Data access via the MCP server
I built an MCP server transforming raw telemetry into a queryable API. Core data operations include:
1. Indexing: Creating efficient contextual field lookups
2. Filtering: Selecting relevant telemetry data
3. Aggregation: Computing measures across time windows
This layer transforms telemetry from an unstructured data lake into a structured interface AI systems can efficiently navigate.
Layer 3: AI-driven analysis engine
The AI component consumes data via MCP, performing:
1. Multi-dimensional analysis: Correlating signals across logs, metrics, and traces.
2. Anomaly detection: Identifying statistical deviations from norms.
3. Root cause determination: Using contextual clues to isolate issues.
Impact of MCP-enhanced observability
Integrating MCP with observability could enhance telemetry data management and understanding. Benefits include:
– Faster anomaly detection, reducing MTTD and MTTR.
– Easier root cause identification.
– Fewer unactionable alerts, reducing alert fatigue and boosting productivity.
– Improved operational efficiency with fewer interruptions during incident resolution.
Actionable insights
Key insights for observability strategy:
– Embed contextual metadata early to aid downstream correlation.
– Structured data interfaces create API-driven, structured telemetry access.
– Context-aware AI focuses on context-rich data for better accuracy.
– Refine context enrichment and AI methods with operational feedback.
Conclusion
Structured data pipelines and AI hold great promise for observability. Leveraging protocols