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

With no annotations provided, the description carries the full burden, and it does well: it discloses accepted input shapes (list vs. object with spans/steps/messages arrays), enumerates supported formats (OTel, OpenAI/LangChain, generic arrays), specifies the {trace} output, and even discloses cost behavior ('Free, no token'). It omits failure/validation behavior, but for a non-destructive transform this is solid coverage.

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?

Three sentences, all front-loaded with the core purpose first, and every sentence earns its place: purpose/benefit, parameter format details, then output and cost signal. The parenthetical format list is dense but compact and directly actionable.

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?

Despite having no output schema and no annotations, the description compensates by specifying the return shape ({trace}) and its intended consumer. For moderate complexity (multi-format input handling), it covers inputs, output, and cost. It only lacks failure-mode and size-limit guidance, which keeps it from a 5.

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 the baseline is 3, but the description adds real value beyond the schema: it expands 'source' by enumerating concrete supported formats (OTel spans, OpenAI/LangChain message lists, {tool,input,output} arrays) and clarifies that the object form may wrap spans/steps/messages/events/logs arrays. This meaningfully reduces an agent's guesswork about what inputs are valid.

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 opens with a specific verb+resource pair ('Turn your raw logs into a Snapback trace') that immediately distinguishes it from siblings like diagnose_trace and detect_loop. It also names its downstream consumer (diagnose_trace), cementing its unique role in the workflow.

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 phrase 'so you don't hand-craft JSON' gives clear when-to-use context, and the 'ready to pass straight to diagnose_trace' line signals the expected workflow. However, it never explicitly names alternatives or states when not to use this tool, so it stops short of full exclusions.

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