A data layer for AI SRE
Give your AI agent live access to your production cluster. Topology, service dependencies, and the full observability stack (metrics, traces, logs, events, profiles) through a single structured surface.
Live application topology
The actual service map of your production cluster: what calls what, with what protocol.
Historical performance
Hours, days, or weeks of metrics, logs, traces, events, and profiles to compare 'now' against.
All telemetry signals
Metrics, traces, logs, events, and profiles. Exposed through a single, structured surface.
What your agent gets access to
The same model the Coroot UI uses, exposed as structured tools instead of dashboards. Read-only and powered by Coroot's RBAC, so each agent sees exactly what its user is allowed to see.
Application topology & dependencies
Live service map of your cluster: what calls what, over which protocol (HTTP, gRPC, SQL, Kafka, Redis), with full SLIs (latency, request rate, failed requests) captured by eBPF, no blind spots.
Metrics
Ad-hoc PromQL plus per-app RPS, error rate, latency percentiles, CPU, memory, and per-host utilization.
Distributed traces
Per-endpoint p50/p95/p99, error reasons with sample trace ids, slow-tail flamegraphs, and full trace-by-id.
Logs
App-scoped or project-wide log search by severity, text, time range, or log pattern.
Events
Alerts, SLO incidents, anomalies, deploys and rollouts, plus DB schema and config changes across the cluster, with the historical timeline.
Profiles
CPU and memory profiles, so the agent can ask why a service slowed down, not just observe that it did.
Connect your agent in one command
Standards-compliant Model Context Protocol over HTTP with OAuth 2.0. The agent runs with the user's Coroot RBAC permissions on every call.
claude mcp add --transport http coroot https://<your-coroot>/mcpcodex mcp add coroot --url https://<your-coroot>/mcpFull setup, auth flow, and the complete tool reference in the documentation.
Make your agent investigate
Coroot Enterprise Edition adds anomaly detection and root-cause investigation. Coroot's deterministic RCA does the heavy lifting before the LLM, so each investigation usually costs under $0.10 in tokens.
Find anomalies across the fleet
The agent gets a single call that returns every app currently in trouble: SLO violations and even sub-SLO error or latency spikes that the alerting rules have not caught yet.
Run a full root-cause investigation
Point the agent at one app and ask why it broke. Coroot walks the dependency graph, checks every candidate cause, and returns the root cause, the immediate fix, and a propagation map showing how the failure spread.
Close the feedback loop on AI-written code
Your agent ships code to production. Coroot tells it what happens next.
How the new version performs
RPS, error rate, and latency percentiles for the freshly deployed pods, side by side with the previous version. The agent can tell whether the change is a regression.
New errors in logs
Coroot's log-pattern detection groups stack traces and surfaces patterns that started appearing only after the deploy, so the agent finds new failure modes without reading every line.
Hot code paths only on production
CPU and memory profiles show which functions the new code actually spends time in at scale, including paths that never showed up in tests. The agent sees the real cost of what it shipped.
Impact on databases
Per-app database stats: connection counts, query rate, slow queries, locks, replication lag. The agent can tell whether the new version put unexpected pressure on Postgres, Redis, MongoDB, or Mysql.
Connect your AI agent to your production
Set up takes one command. The agent gets the full Coroot model: topology, SLIs, traces, logs, metrics, events, and profiles.
✓ Standard MCP over HTTP ✓ OAuth 2.0 ✓ Works with any MCP-compatible client