incident-commander-mcp
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@incident-commander-mcpInvestigate the error spike in payment-service around 14:03 UTC"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
incident-commander-mcp
AI-powered incident management MCP server built for the NitroStack Hackathon.
⚠️ R12 Disclosure — Simulated Integrations
All data returned by this MCP server is entirely mocked and stored in-memory.
There are NO real connections to:
Kubernetes / container orchestration platforms
Grafana / Prometheus / any metrics backend
GitHub / GitLab / any version control system
PagerDuty / OpsGenie / any alerting system
Stripe or any payment processor
This is a demo for hackathon evaluation purposes. The realistic-looking data is seeded with a purposeful incident scenario (deploy at 14:02 → error spike at 14:03) to demonstrate agent reasoning capabilities over MCP tools, resources, and prompts.
Overview
incident-commander-mcp is a single MCP server built on the NitroStack TypeScript framework
(@nitrostack/core). It exposes tools, resources, and prompts that enable an AI agent to:
Investigate production incidents by querying deployment history, error logs, and metrics
Reason about root causes using cross-correlated data
Act (with human approval) by requesting rollbacks or creating incident tickets
Document incidents via structured postmortem prompts
Capabilities
🔧 Tools (8)
Tool | Description | Annotation |
| Recent deployments, optionally filtered by service |
|
| Error logs for a service within a time window |
|
| Latency, error rate, CPU/memory time-series |
|
| Kubernetes pod readiness and restart counts |
|
| Cross-correlates deploy + logs + metrics → hypothesis |
|
| Submits a rollback request (requires human approval) |
|
| Creates a new in-memory incident record | — |
| Appends a timeline entry to an incident | — |
📦 Resources (4)
URI | Description |
| Active incident snapshot (JSON) |
| Timeline for a specific incident |
| Last 20 deployments across all services |
| Health snapshot for one service |
💬 Prompts (3)
Prompt | Arguments | Description |
|
| Guides structured RCA with tool call sequence |
|
| Generates a blameless postmortem |
|
| Risk/benefit analysis before rollback |
Incident Scenario (Demo)
The seeded data tells a realistic story:
14:00 UTC — payment-service healthy: error_rate 0.2%, latency 45ms
14:02 UTC — payment-service v2.4.1 deployed (commit a3f8c21d)
14:03 UTC — Error spike: "Cannot read properties of undefined (reading 'stripeId')"
14:03 UTC — error_rate jumps to 28%, latency spikes to 320ms
14:04 UTC — Pods begin CrashLoopBackOff, 7 restarts
14:05 UTC — error_rate: 91%, latency: 1450ms, API Gateway circuit breaker opensAn agent reasoning over get_recent_deployments → get_service_logs → get_service_metrics →
analyze_root_cause should produce a hypothesis pointing to v2.4.1 as the culprit with ~0.90 confidence.
Quick Start
# 1. Clone / navigate to project
cd incident-commander-mcp
# 2. Install dependencies (already done by CLI)
npm install
# 3. Start development server
npm run devBuilding for Production
npm run build
npm startBuilt with NitroStack ⚡
This server cannot be installed
Maintenance
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