Last9 MCP Server enables AI agents to query exception and service graph data from Last9's observability platform. The implementation provides two tools: get_exceptions for retrieving server-side exceptions with details like type, message, and stack trace; and get_service_graph for analyzing upstream and downstream service dependencies with throughput metrics. Built with Go and featuring configurable rate limiting, the server integrates seamlessly with Claude desktop app and Cursor, making it valuable for developers who need AI-assisted troubleshooting and service dependency analysis within their Last9 monitoring environment.
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Highlight AIRanked fleet (service, env) rows: interval request_count, throughput_rpm, HTTP 4xx/5xx counts, and gRPC error counts.
Available environments for your services. Run this first — other APM tools need env from here.
Full breakdown: throughput, error rate, p50/p90/p95/avg/max, apdex, availability.
Operations grouped by HTTP endpoints, DB calls, messaging, HTTP clients.
Dependency map with throughput, latency, and error rates for upstream/downstream/infra.
Compare a current window against an equal-duration baseline: regressions/improvements, Apdex reconciliation, and a terminal outcome.
Server-side exceptions with service and span filters.
Discover all databases across your infrastructure: DB type, host, throughput (queries/min), p95 latency, error rate, number of dependent services.
The actual slowest query executions, ordered by duration, with trace IDs for drilling into full traces.
Query patterns and aggregates: how often a query runs, average/p95 duration, error rate.
Server-side metrics from the DB host itself (CPU, connections, buffer hit rates — depends on your DB system).
PromQL range queries over any metric.
Instant queries; use rollup functions like avg_over_time, sum_over_time.
Label values for a given series.
All labels available for a series.
Full JSON pipeline log queries (aggregations, filters, field extraction).
Raw log lines for a service, filterable by severity and body content.
Global catalog of attributes in the log schema for a time window.
Log fields actually present for an in-progress pipeline (scoped discovery), each with its exact filter_field.
Log drop rules from Last9 Control Plane.
Create a new drop rule to cut log volume at the source.
JSON pipeline trace queries for broad searches and aggregations.
Traces by exact trace ID or service name.
Global catalog of attributes in the trace schema.
Attributes actually present for an in-progress pipeline (scoped discovery), each with its exact filter_field.
Distinct values for a trace attribute, optionally scoped to a pipeline.
Ranks attribute values that differ between two bounded span cohorts.
One exact trace as a parent/child waterfall with interval-union self-time, slowest spans, and graph warnings.
Deployments, config changes, rollbacks. Correlate incidents with what changed.
Alert rule configurations — searchable by name, severity, type, tags.
Currently firing alerts within a time window.
Historical firing state per alert rule over a time range.
Configured notification channels (Slack, PagerDuty, email, etc.).
All custom dashboards in your org: IDs, names, and metadata.
Full dashboard definition by ID, including panels and queries.
Create a new custom dashboard with panels, queries, and metadata.
Update an existing dashboard by ID.
Delete a custom dashboard by ID.
Frozen point-in-time snapshots for a dashboard.
Full frozen snapshot including panel data for RCA/shareable views.
Delete a frozen snapshot by ID.
When the agent isn't sure about an entity name, this returns the closest matches from your catalog.
What a service's telemetry actually looks like, before you query it.
