import_artifact

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

Beyond annotations, the description discloses automatic trace detection ('identified automatically') and ambiguity handling ('set producer='torch.profiler' for ambiguous'), adding useful behavioral context. Annotations indicate a non-readonly, non-idempotent, non-destructive operation, and the description does not contradict this.

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?

The description is concise (three sentences), front-loaded with the primary purpose, and each additional sentence provides actionable guidance without redundancy. No fluff or tangential details.

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 the tool's complexity (7 parameters, 3 required) and the presence of an output schema, the description covers the critical workflow context: import → extract_perfetto → analyze_pytorch. It doesn't explain all edge cases (e.g., import conflicts), but the schema and sibling context fill most gaps.

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 high (86%), with detailed parameter descriptions already present. The description adds contextual value by mapping kind to execution_trace for Chrome/Torch traces and producer to torch.profiler for ambiguous cases, which is beyond the schema's generic enum descriptions.

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 states a specific action and resource: 'Import one project-local artifact and preserve producer identity.' This clearly distinguishes the tool from sibling extract/analyze tools by focusing on the import step and identity preservation.

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?

Provides explicit procedural guidance: 'Use kind='execution_trace' for Chrome/Torch traces, then run extract_perfetto before analyze_pytorch' and 'If detection is ambiguous, set producer='torch.profiler'.' This tells the agent exactly when and how to use the tool, including naming the next pipeline steps.

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