Training Pipeline Scan

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

Annotations already declare readOnlyHint=true and destructiveHint=false. The description confirms the read-only scanning behavior but adds no further behavioral context (e.g., performance, file system access). It adds moderate value beyond annotations.

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?

Single sentence (12 words) that is front-loaded with action and resource. Every word adds value; no redundancy.

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?

For a simple scanning tool with one parameter and output schema present, the description sufficiently covers the purpose. However, it omits any note about supported artifact formats or depth of scanning, but the schema and output likely fill those 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 includes a parameter description covering directory content (MLflow, Kubeflow, W&B artifacts). The tool description adds no additional parameter meaning beyond restating the schema. With 100% schema coverage, baseline 3 is appropriate.

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 the action (scan), resource (directory), and specific purpose (ML training pipeline lineage and provenance). It distinguishes from sibling tools like code_scan or model_file_scan by focusing on training pipeline artifacts.

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?

No guidance on when to use this tool versus alternatives (e.g., model_provenance_scan, dataset_card_scan). The description does not mention prerequisites or exclusions.

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