Training Pipeline Scan

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

Annotations already declare the tool as read-only, non-destructive, idempotent, and open-world, so the description does not need to repeat these. However, it adds minimal behavioral context beyond the schema, such as what the scan entails (e.g., handling of missing directories or performance implications). The description is consistent with annotations, so no contradiction.

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 a single concise sentence that front-loads the key action and target. It contains no redundancy or unnecessary detail, making it efficient for the agent to parse.

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 simplicity (1 parameter, no nested objects) and the presence of an output schema, the description covers the essential information. It does not explicitly mention recursive scanning, but the schema's directory parameter implies directory traversal. Overall, it is sufficiently complete for an agent to understand the tool's purpose.

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?

The single parameter 'directory' is fully described in the input schema with a clear explanation of its purpose and expected artifacts (MLflow, Kubeflow, W&B). The description does not add extra meaning beyond the schema, meeting the baseline for high schema coverage.

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 tool scans a directory for ML training pipeline lineage and provenance, distinguishing it from sibling scan tools that target other artifacts like models (model_file_scan) or code (code_scan). However, it does not explicitly differentiate from model_provenance_scan, which may have overlapping scope.

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 description provides no guidance on when to use this tool versus alternatives. It does not mention prerequisites, scenarios where it is appropriate, or when to avoid it. Sibling tool names hint at differentiation, but the description itself lacks usage guidance.

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