Machine Context Protocol (MCP) for Observability & Performance

This section catalogs specialized MCP services that accelerate expert workflows across observability, tracing, and performance engineering.

Goals:

• Shorten MTTI / MTTR by automating high-cognitive investigation loops
• Normalize input/output contracts for safe automation in CI / Chat / IDE agents
• Provide repeatable playbooks mapped to concrete MCP capabilities

Structure:

1. Observability MCPs – metrics, logs, profiling queries
2. Tracing MCPs – latency, critical path, anomaly detection, correlation
3. Performance MCPs – benchmarks, flamegraphs, resource pressure analytics
4. Cross-Cutting MCPs – SLOs, cost, capacity, aggregated recommendations
5. Patterns – composition, chaining, governance
6. References – repos, schemas, examples
7. Tutorials – step-by-step implementations

Each MCP spec below follows a consistent template: Field | Description ------|------------ Name | Canonical identifier Problem | Pain addressed / latency in traditional workflow Inputs | Required parameters (typed) Outputs | Structured fields (JSON) + optional artifacts Data Sources | Systems queried (Prometheus, Pyroscope, Tempo, etc.) Algorithm | Core heuristic / model approach Failure Modes | Expected errors + mitigation Security | Auth scopes, least privilege Example | Minimal invocation Extension | Natural evolution / advanced variant

Use these MCPs as modular building blocks in: CI pipelines, on-demand chat copilots, pre-merge performance gates, automated incident retros.