graph_lifecycle

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

No annotations provided, so description carries full burden. It indicates read-only query behavior (showing timeline) but does not explicitly state safety, permissions, or side effects. The description is adequate but lacks definitive behavioral traits.

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 sentences, each adding value: first defines action and output, second provides context. No fluff, front-loaded with key information.

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?

Given no output schema and no annotations, description should cover output format. It mentions timestamps and durations but not structure (e.g., list, graph). Also lacks limitations or edge cases. Adequate but incomplete for a tool with no output schema.

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 3. Description does not add significant meaning beyond schema; it mentions PID implicitly but does not elaborate on tsc or since parameters. The description adds context about output (timestamps, durations) but not parameter details.

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?

Description clearly states it shows CUDA Graph lifecycle timeline for a PID, specifying sequences (capture, instantiate, launch) with timestamps and durations. It differentiates from sibling tools like graph_frequency by focusing on timeline rather than frequency.

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?

Description implies usage for analyzing graph activity in PyTorch workloads but does not provide explicit when-to-use or when-not-to-use guidance, nor does it mention alternatives like graph_frequency or other tools.

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