Jaeger MCP vs MCP Grafana

jaeger_search_traces

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`).

jaeger_get_trace

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``.

jaeger_get_dependencies

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}``).

jaeger_compare_traces

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``.

jaeger_span_statistics

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).

jaeger_critical_path

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.

jaeger_compare_windows

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.

jaeger_detect_anomalies

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.

jaeger_find_test_traces

Find Jaeger traces matching the supplied tag query. Accepts any tag key-value schema (Allure, pytest, custom) without normalization. When service is omitted, searches all known services concurrently (capped at 20). Results are sorted newest-first.

jaeger_regression_diff

Compare two Jaeger time windows and classify per-operation regressions. Fetches traces from the baseline and comparison windows, then classifies each operation as regressed, recovered, appeared, or removed. Results are sorted by severity score (0-100) descending for easy triage.