Build Infrastructure Context for Your Coding Agent
Open-source CLI that syncs metadata from Grafana, Datadog, Kubernetes, and 20+ tools into structured markdown — so your AI agent stops hallucinating and starts debugging accurately.
Works with Claude Code, Cursor, Windsurf, and any agent that reads context files.
$ curl -fsSL https://raw.githubusercontent.com/DrDroidLab/context-builder/main/install.sh | bash
Also available via pipx install
With vs Without Infrastructure Context
Without droidctx
Debug why payment latency spiked
Checking Kubernetes pods... (service is on ECS)
Writing PromQL from scratch... (dashboard exists)
Found redis-test-instance... (wrong instance)
Agent gave up after 15 tool calls.
With droidctx
Debug why payment latency spiked
payment-service runs on ECS (from context)
Checking "Payment Service Overview" in Grafana
Redis prod metrics show p99 spike at 14:32
Upstream: api-gateway, Downstream: notification-svc
Root cause identified in 4 tool calls.
See It In Action
Watch how droidctx builds infrastructure context for your coding agent in minutes.
DroidCTX: An Open Source CLI Tool that gives infrastructure context to Coding agents / Claude Code
What Gets Generated
A structured directory of markdown files — click through to see what your agent gets.
droidctx-context
File TreePreview
droidctx-context overview.md connectors cross_references overview.md
# Infrastructure Overview
## Connectors Synced
- grafana_prod (Grafana) — 12 dashboards, 34 alerts
- k8s_production (Kubernetes) — 3 namespaces, 18 deployments
- datadog_prod (Datadog) — 8 monitors, 5 dashboards
- postgres_main (PostgreSQL) — 24 tables
- ecs_production (ECS) — 6 services, 3 clusters
## Services Discovered: 14
## Cross-references Generated: 23
Click files in the tree to preview their contents. This is generated from your live infrastructure.
Production Is Too Vast to Guess
A typical production setup has dozens of services spread across Kubernetes, ECS, and Lambda — with metrics in Grafana, alerts in Datadog, schemas in PostgreSQL, and deploys in ArgoCD. No agent can guess this topology.
- 5-10 Observability & infra tools per team on average
- 50+ Dashboards, alerts & monitors to navigate
- 0 Of this is known to your AI agent by default
Your agent is limited by what you know to tell it
Without context files, the agent can only navigate what you manually describe in your prompt. If you don't know that Redis metrics are in a specific Grafana dashboard, the agent won't either. It's restricted by your knowledge of where things live across a multi-tool system — which means it works for you, but can't be handed to a teammate who has different tribal knowledge.
Without a map, the agent wanders
When the agent doesn't know what exists — which services run where, which dashboards track what, how services connect — it guesses. It checks Kubernetes when the service is on ECS. It writes PromQL from scratch when a dashboard already has the panel. It finds a test Redis instance instead of production. Every wrong turn costs tool calls, tokens, and time.
How It Works
Four commands. That's it.
Initialize
Creates a credentials template for your infrastructure tools.$ droidctx initAuto-Detect
Scans forkubectl,aws,gcloud, andazconfigs and auto-populates credentials.$ droidctx detectSync
Extracts metadata from all connected tools and generates structured markdown files with cross-references.$ droidctx syncPoint Your Agent
Reference the generated context in your CLAUDE.md or agent config. Your agent now has a complete map.# CLAUDE.md Reference ./droidctx-context/ for infra context
25+ Connectors
Syncs context from the tools your team already uses.
Grafana Datadog New Relic Kubernetes PostgreSQL MongoDB Elasticsearch AWS GitHub Sentry Jenkins ArgoCD CloudWatch Jira PostHog SigNoz GKE Google Cloud ClickHouse Prometheus Victoria Metrics Grafana Loki OpenSearch Coralogix Azure
Give Your Agent Infrastructure Context
Install in 30 seconds. Generate context in one command. Debug accurately from day one.