compare_traces

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

No annotations are provided, so the description carries the full burden. It discloses the analysis steps (aligns by sequence, finds first divergence, summarises network differences) and implies a read-only comparison. However, it does not describe output format, needed permissions, or edge cases (e.g., traces not from same test). This is moderate transparency.

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 compact sentences that front-load the main action and follow with specifics. Every sentence adds value, with no filler.

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?

The description covers purpose, method, and use case. It lacks output details, but the tool has no output schema and siblings likely share similar output patterns. Given only two well-documented parameters, the description is sufficiently complete for an AI agent to select and invoke it correctly.

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?

Input schema descriptions are minimal but cover 100% of parameters. The tool description adds the semantic relationship that the two paths are passing and failing traces of the same test, which is helpful but not extensive. 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?

The description clearly states a specific verb ('Compares') with the resource ('a passing and a failing trace of the same test') and details the comparison approach (aligns actions, finds divergence, summarises network differences). This distinguishes it from sibling tools like get_trace_summary or get_action_timeline which operate on a single trace.

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 its use case: 'Use to diagnose flakiness — what was different in the run that failed.' It communicates when to apply the tool, though it does not mention exclusions or alternative sibling tools. Context is clear.

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