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

Annotations already declare readOnlyHint=true and destructiveHint=false, so safety is covered. The description adds genuine behavioral context beyond that: the tool degrades gracefully by using trace data when available and falling back to transcript-based diagnosis otherwise, which tells the agent what it can expect from different runs.

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?

Two tight sentences: the core capability and its outputs come first, followed by availability and data-source behavior. No filler or repetition.

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?

With no output schema, the description carries the return-value burden and does so well by naming the four diagnostic components. It is nearly complete, though it omits return format or pagination/size expectations, leaving a small gap.

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 description coverage is 100%, so both parameters (customerId, conversationId) are already documented in the schema. The description adds nothing parameter-specific, so the baseline 3 applies.

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?

States a specific verb and resource ('Get step-level failure diagnosis for a run') and enumerates the concrete outputs: first failing step, cascade effects, root cause hypothesis, per-turn timeline. This is clearly distinguishable from general run tools like get_run, though it never explicitly names a sibling such as get_step_failure_aggregation to sharpen the contrast.

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?

The description states availability ('Available for all runs') and the data source fallback, which implies the tool is always applicable, but it gives no explicit when-to-use versus alternatives or when the aggregation sibling is preferred. Usage is implied rather than directed.

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