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jaeger_critical_path

Read-onlyIdempotent

Identify the critical path and top bottlenecks in a distributed trace to pinpoint performance issues.

Instructions

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.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
trace_idYesTrace ID as a hex string (16 or 32 hex chars). Obtain from jaeger_search_traces.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
trace_idYes
bottlenecksYes
critical_pathYes
root_operationYes
bottleneck_countYes
total_duration_usYes
critical_path_percentageYes
critical_path_duration_usYes
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds valuable context: it finds the longest-duration span chain, ranks spans by self-time, and returns a dict with trace metadata, critical path spans, and bottleneck ranking. No contradictions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise, front-loaded with the purpose, and structured with bullet points for usage examples, non-usage cases, and return value. Every sentence adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity, single parameter, existing output schema, and thorough annotations, the description is complete enough for an agent to understand when and how to use it.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100% for the single trace_id parameter, which is well-described in the schema. The tool description adds no extra parameter information beyond what the schema provides, so baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool identifies the critical path and top bottlenecks in a trace. It distinguishes from siblings by explicitly noting when not to use (e.g., for aggregate statistics or comparing traces) and naming alternative tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Multiple 'Use when' examples are provided, each with a clear scenario and expected action. 'Don't use when' cases specify alternative sibling tools (jaeger_span_statistics, jaeger_compare_traces), offering explicit guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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