List all services that Jaeger has observed traces for.

Wraps GET /api/services. Jaeger returns all services at once — no pagination. Output is capped at 500 services with a truncation hint.

Use this first to discover valid service names before calling jaeger_list_operations or jaeger_search_traces.

Examples: - Use when: "What services does Jaeger know about?" → call with no parameters; read the services list. - Use when: "Is payment-service instrumented?" → check if payment-service appears in the services list. - Use when: Starting a debugging session — list services first, then pick one for jaeger_list_operations or jaeger_search_traces. - Don't use when: You already know the service name and want to search its traces (call jaeger_search_traces directly). - Don't use when: You want the dependency graph between services (call jaeger_get_dependencies).

Returns: dict with keys services_count / truncated / services.

List all operation names Jaeger has seen for a given service.

Wraps GET /api/services/{service}/operations. Useful for discovering which operation names to pass as filters to jaeger_search_traces. Output is capped at 500 operations.

Examples: - Use when: "What HTTP endpoints does order-service expose in tracing?" → service='order-service'. - Use when: You want to search for a specific slow operation but need the exact name — list operations first, then pass it to jaeger_search_traces. - Use when: Auditing which gRPC methods a service traces. - Don't use when: You don't have a specific service — start with jaeger_list_services first. - Don't use when: You want to search traces immediately (skip this step if you already know the operation name).

Returns: dict with service / operations_count / truncated / operations (sorted alphabetically).

Search Jaeger traces with rich filters.

Wraps GET /api/traces. Returns a list of trace summaries — use jaeger_get_trace to drill into a specific trace for span details.

The tags parameter accepts a JSON string so the LLM can construct arbitrary tag filters. Durations (min_duration/max_duration) are forwarded as-is to Jaeger (e.g. '100ms', '1.5s').

Examples: - Use when: "Show me recent 500 errors in order-service" → service='order-service', tags='{"http.status_code":"500"}'. - Use when: "Find slow traces (>1s) for checkout endpoint" → service='checkout', operation='POST /checkout', min_duration='1s'. - Use when: "Give me the last 5 traces in the last hour" → limit=5, set start to (now - 3600s) in microseconds. - Don't use when: You already have a traceID and want full details (call jaeger_get_trace directly — one fewer round trip). - Don't use when: You want service dependency topology (call jaeger_get_dependencies).

Returns: dict with service / operation / returned / truncated / traces (list of :class:TraceSummary).

Retrieve full trace detail with all spans, service breakdown, and execution tree.

Wraps GET /api/traces/{traceID}. Returns every span in the trace, per-service statistics, and a flat execution tree (each node lists its child span IDs) that summarises the call hierarchy.

Error spans are identified by tags["error"] = "true".

Examples: - Use when: "Why is trace abc123... slow — show me the span breakdown" → trace_id='abc123...'; inspect services for the heaviest service and execution_tree for the call hierarchy. - Use when: "Which service caused the error in trace xyz...?" → check spans where is_error=true. - Use when: You found a slow/failed trace in jaeger_search_traces and need full detail. - Don't use when: You don't have a specific traceID — use jaeger_search_traces to find one first. - Don't use when: You only want aggregate data across many traces (use jaeger_search_traces with filters instead).

Returns: dict with trace_id / span_count / service_count / root_operation / root_service / start_time_us / total_duration_us / errors_count / services (per-service stats) / spans (all spans) / execution_tree.

Retrieve the service-to-service call graph from Jaeger.

Wraps GET /api/dependencies. Returns directed edges (parent → child) with call_count — the number of spans where parent called child in the lookback window.

Use this to understand service topology, find high fan-out services, or verify that a new service is connected as expected.

Examples: - Use when: "What services does order-service call?" → check edges where parent='order-service'. - Use when: "Map the full service dependency graph for the last 7 days" → lookback_hours=168. - Use when: "Which services are called most frequently?" → sort edges by call_count descending. - Don't use when: You want detailed span timings (use jaeger_search_traces + jaeger_get_trace instead). - Don't use when: You need real-time data — Jaeger's dependency graph is aggregated and may lag by minutes.

Returns: dict with end_ts_us / lookback_hours / edge_count / edges (list of {parent, child, call_count}).

Compare two traces structurally — find added, removed, and changed spans.

Fetches both traces from Jaeger and performs a structural diff by matching spans on (operationName, serviceName, parentOperation) — not span IDs, which differ across traces. Reports duration deltas and tag differences for changed spans.

Examples: - Use when: "What changed between a fast and slow request?" → pass the trace IDs of both requests; inspect changed_spans for duration deltas. - Use when: "Did a deployment add new service calls?" → compare a pre-deploy trace with a post-deploy trace; check added_spans for new operations. - Use when: "Are these two traces structurally identical?" → if added_spans, removed_spans, and changed_spans are all empty, the traces have the same structure. - Don't use when: You want aggregate statistics across many traces (use jaeger_span_statistics instead, once available). - Don't use when: You only have one trace — use jaeger_get_trace for single-trace inspection.

Returns: dict with trace_id_a / trace_id_b / added_spans / removed_spans / changed_spans (with duration + tag deltas) / unchanged_count.

Compute per-operation latency percentiles and error rates across recent traces.

Fetches up to limit traces for the given service (optionally filtered by operation), then aggregates all spans by operation name. For each operation reports: span count, p50/p95/p99 duration in microseconds, error count, and error rate.

Duration values are in microseconds (integer). Error rate is error_count / span_count (float, 0.0–1.0).

Examples: - Use when: "What are the p95 latencies for each endpoint in order-service?" → service='order-service'; inspect each operation's p95_duration_us. - Use when: "How often does the POST /checkout endpoint error?" → service='checkout-svc', operation='POST /checkout'; check error_rate in the stats. - Use when: "Compare latency distributions across operations" → look at p50 vs p99 spread to identify high-variance operations. - Use when: "Get a larger sample for more accurate stats" → limit=100 for higher confidence percentiles. - Don't use when: You want to compare two specific traces (use jaeger_compare_traces instead). - Don't use when: You want full span detail for a single trace (use jaeger_get_trace instead).

Returns: dict with service / operation / trace_count / stats (list of per-operation stats with count, p50/p95/p99 duration_us, error_count, error_rate).

Identify the critical path and top bottlenecks in a trace.

Finds the longest-duration span chain (critical path) from root to leaf, and ranks spans by self-time to find actual performance bottlenecks.

Examples: - Use when: "Why is this trace so slow?" → call with the slow trace ID; examine the critical_path_duration_us and critical_path_percentage to see how much of the total time is spent on the longest path. - Use when: "Which operations are consuming the most CPU/self-time?" → check the bottlenecks list sorted by self_time_us descending. - Use when: Debugging performance regressions — compare critical path percentages before/after changes. - Don't use when: You want aggregate statistics across many traces (use jaeger_span_statistics for that). - Don't use when: You need to compare two traces structurally (use jaeger_compare_traces for that).

Returns: dict with trace metadata, critical path spans, and bottleneck ranking.

Compare aggregate trace behavior between two time periods for a service.

Fetches traces from both time windows, aggregates span statistics per operation, then compares the aggregate behavior to detect performance changes.

Examples: - Use when: "Did our latest deployment affect performance?" → compare pre-deploy and post-deploy time windows for the service. - Use when: "Which operations got slower after the database upgrade?" → check the comparison_p95_us and p95_delta_pct columns for increases. - Use when: "Are we seeing new error patterns?" → look for operations with increased error_rate_delta. - Use when: "Did we add or remove any API endpoints?" → check added_count and removed_count in the summary. - Don't use when: You want to compare two specific traces (use jaeger_compare_traces instead). - Don't use when: You want full span detail for a single trace (use jaeger_get_trace instead).

Returns: WindowComparisonOutput with per-operation diffs and summary statistics.

Detect latency and error-rate anomalies for a service by comparing recent behavior to historical baseline.

Fetches traces from a historical baseline window and a recent observation window, computes per-operation statistics for both, then identifies statistically significant deviations that may indicate performance issues or reliability problems.

Examples: - Use when: "Are there any new performance issues in order-service?" → service='order-service' (uses default 60-minute baseline, 5-minute current). - Use when: "Be more sensitive to subtle changes" → set sensitivity=1.5 (lower threshold). - Use when: "Check for issues over the last 24 hours against previous week" → baseline_duration_minutes=10080, current_duration_minutes=1440. - Don't use when: You want to compare two specific time periods (use jaeger_compare_windows instead). - Don't use when: You want full span detail for a single trace (use jaeger_get_trace instead).

Returns: AnomalyDetectionOutput with flagged operations and severity scores.

Predict potential performance degradation events for a service.

Analyzes historical trace data patterns, critical path trends, and anomaly detection results to forecast likely performance issues 2-24 hours in advance.

Args: service: Service name to analyze for potential degradation hours_back: Number of hours of historical data to analyze (default: 168 hours/1 week)

Returns: PredictionResult with degradation forecast, confidence level, and recommendations

Forecast future throughput demands and resource requirements for a service.

Provides predictions for the next 7-30 days with confidence intervals to enable infrastructure scaling decisions.

Args: service: Service name to forecast capacity for days_ahead: Number of days to forecast ahead (default: 30 days)

Returns: ForecastResult with throughput predictions and resource requirements