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get_traces

Analyze individual trace samples in JSON format to identify bottlenecks and debug performance issues in specific slow requests. Filter by environment, service, transaction name, response time, and date range.

Instructions

Analyze individual trace samples in JSON format for detailed bottleneck identification and performance debugging. Use for investigating specific slow requests, not system-wide statistics (use get_performance_metrics for aggregate data).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
envNoEnvironment name (e.g., "production", "staging")
sNoService name (e.g., "web", "api", "worker")
transaction_nameNoFilter by specific transaction/endpoint name
has_callgraphNoOnly return traces with detailed callgraph data
searchNoWord-based search on transaction_name, host, and URL text tokens. This is no fulltext search.
min_dateNoMinimal date for traces in YYYY-MM-DD HH:MM format (e.g., "2024-01-15 14:30"). Convert natural language like "1 hour ago" to this format. Requires max_date.
max_dateNoMaximal date for traces in YYYY-MM-DD HH:MM format (e.g., "2024-01-15 16:30"). Convert natural language like "now" to this format. Requires min_date.
min_response_time_msNoMinimum response time in milliseconds for filtering slow traces
max_response_time_msNoMaximum response time in milliseconds for filtering traces
sort_byNoField to sort traces byresponse_time
sort_orderNoSort order (DESC = slowest/newest first)DESC
Behavior2/5

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

No annotations provided, so description carries full burden. It only mentions JSON output and purpose, but lacks details on returned volume, pagination, or potential side effects.

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?

Two concise sentences, front-loaded with purpose and usage, no superfluous text.

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

Completeness3/5

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

Given 11 parameters, no output schema, and no annotations, description covers purpose and usage well but lacks behavioral details like pagination or return format. Schema descriptions fill parameter details, but completeness is moderate.

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 has 100% description coverage, so description adds minimal extra value beyond schema. 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?

Description clearly states the tool analyzes individual traces for bottleneck identification, and explicitly differentiates from get_performance_metrics for aggregate data.

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?

Directly states when to use (specific slow requests) and when not (system-wide statistics), naming the sibling alternative.

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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