query_traces

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

States 'Behavior: read-only' aligning with annotations (readOnlyHint=true). Adds details about filter language, errorsOnly behavior, default limit, and what happens if no backend is configured. Provides useful behavioral context beyond annotations.

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

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

Well-structured with clear sections. Purpose is front-loaded. Every sentence adds value; no repetition or fluff. Appropriate length for the complexity of the tool.

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

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

Description covers return value format (ranked summaries with p50/p95), parameter details, prerequisites, and error case. Despite no output schema, the description provides sufficient contextual completeness for correct tool invocation.

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

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

Schema coverage is 100%, so baseline is 3. The description adds value by explaining that filter accepts native query language (e.g., TraceQL) and mentions default limit, going beyond individual parameter descriptions. However, it could have included more detail on duration format or error status meaning.

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

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

Describes specific verb 'Query' and resource 'distributed traces for a service' with output details (ranked summaries, aggregates). Clearly distinguishes from sibling tools like query_logs and query_metrics.

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

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

Explicitly states when to use (tail-latency, call chains, anomaly follow-up), prerequisites (service name from list_services, connector configuration), and a failure mode (no backend configured). Offers clear usage context.

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