ac50ee7ad8
- guide/observability.md: +3 sections (Activity Monitoring, External Tools, Proxying) - Activity Monitoring: JSONL tool_use audit, jq queries, sensitive pattern detection - External Tools: ccusage / claude-code-otel / Akto / MLflow / ccboard comparison - Proxying: NODE_EXTRA_CA_CERTS, ANTHROPIC_API_URL, mitmproxy, Python proxy - docs: ccboard Activity module implementation plan (Tab 10, Rust models, SQLite cache) - docs: Mergify cross-system support evaluation Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2.6 KiB
2.6 KiB
Resource Evaluation: Mergify — Cross-System Support Investigator
Date: 2026-02-20 Evaluator: Claude (eval-resource skill) Score: 4/5 Action: Integrated
Source
- URL: https://mergify.com/blog/how-we-turned-claude-into-a-cross-system-support-investigator
- Author: Julian Maurin (Mergify)
- Date: November 2025
- Type: Engineering blog post / production case study
Summary
Mergify built a support ticket investigation system combining Claude Code as orchestrator with 5 custom MCP servers (Datadog, Sentry, PostgreSQL, Linear, GitHub). The system executes parallel queries across all systems and produces a structured report for human review.
Key results (self-reported): triage time reduced from ~15 min → <5 min; 75% first-pass accuracy.
Scoring
| Criterion | Assessment |
|---|---|
| Relevance to guide | High — fills gap on MCP operational orchestration |
| Novelty vs. existing content | High — "Claude Code as ops runtime" not covered |
| Production evidence | Medium — self-reported metrics, no public repo |
| Technical depth | Medium — architecture clear, code not published |
| Source reliability | Medium — company blog (potential marketing bias) |
Final score: 4/5
Integration
- Where:
guide/ultimate-guide.md— §8.4 Server Selection Guide → "Combining Servers" → new subsection "Production Case Study: Multi-System Support Investigator" - Angle: Claude Code as operational orchestrator (ops/support use case, not dev tool)
- Key addition: Architecture diagram, parallel fan-out pattern, design decisions, results with caveat
Fact-Check
| Claim | Status | Notes |
|---|---|---|
| Author: Julian Maurin | ✅ | Confirmed via Perplexity deep research |
| Date: November 2025 | ✅ | Confirmed |
| 5 systems: Datadog, Sentry, PostgreSQL, Linear, GitHub | ✅ | Confirmed |
| Triage: 15 min → <5 min | ⚠️ | Self-reported, not independently verified |
| First-pass accuracy: 75% | ⚠️ | Self-reported, same caveat |
| Architecture: MCP + parallel execution | ✅ | Consistent with MCP ecosystem |
Metrics are presented in the guide with "self-reported" caveat.
Challenge Notes (technical-writer agent)
- Score 4/5 confirmed
- 75% accuracy = 25% still need human follow-up → presented as "assistance", not "automation"
- Source bias noted (Mergify sells CI/CD automation, article serves their brand)
- Real value: "orchestrateur stateful avec MCP adaptateurs" pattern, distinct from all existing guide cases
- Risk of non-integration: moderate — guide will lag on ops/support angle as similar cases multiply