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find_slow_traces

Identify slowest traces causing high latency. Filter by threshold, top N, or group by agent, user, or domain to pinpoint performance bottlenecks.

Instructions

Find the slowest traces. Returns actual trace IDs and metadata.

Use this to answer: 'Which traces were slowest?', 'Show me traces taking >30s', 'What's causing high latency today?'.

domain: filter by email domain (e.g. 'acme.com'). If threshold_seconds is set, returns all traces above that threshold. Otherwise returns the top_n slowest traces. group_by: 'name' (agent), 'userId', 'domain'.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tagsNo
top_nNo
domainNo
projectNo
end_dateNo
group_byNo
start_dateNo
time_rangeNo
threshold_secondsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

With no annotations provided, the description must carry the full burden of behavioral disclosure. It does explain the key behavioral logic: 'If threshold_seconds is set, returns all traces above that threshold. Otherwise returns the top_n slowest traces.' It also explains the group_by options. However, it doesn't disclose behavior for other parameters like time_range or tags, which is a notable gap.

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 compact and well-structured. It starts with the primary purpose, provides example queries, and then explains key parameters in a terse, scannable format. Every sentence adds value without redundancy.

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

Completeness4/5

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

The description gives enough context for the core functionality and typical use cases, including the threshold vs. top_n behavior and group_by semantics. The existence of an output schema means return values need not be described. However, with 9 parameters and no annotations, guidance on the time-range and project filters would improve completeness.

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?

The schema has 0% description coverage, so the description must compensate. It explains domain, threshold_seconds, top_n, and group_by, which is valuable. However, it leaves 5 out of 9 parameters (tags, project, start_date, end_date, time_range) completely unexplained, creating a significant gap in parameter understanding.

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's function: 'Find the slowest traces. Returns actual trace IDs and metadata.' This uses a specific verb and resource, and the mention of returning actual trace IDs helps distinguish it from aggregated analytics tools like analyze_latency. Example questions further reinforce the purpose.

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

Usage Guidelines4/5

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

The description provides clear context for when to use the tool via example queries ('Which traces were slowest?', 'Show me traces taking >30s'), which implies the use cases. However, it doesn't explicitly mention alternatives or when not to use it, so it falls short of a perfect score.

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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