dbt-list-models

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

No annotations provided; description does not explicitly state read-only nature or any side effects. Does not disclose performance considerations or data freshness.

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, front-loaded, no wordiness. Could be slightly more informative about return structure.

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?

No output schema; does not describe returned fields. Adequate for a list tool but leaves questions about output format and project path dependency.

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?

Input schema has 100% description coverage; description only lists parameter names without adding new meaning. Baseline 3 applies; marginal value added.

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?

Clearly states verb (List), resource (dbt models), source (manifest.json), and filter options (package, tag, etc.). Distinguishes from singular 'dbt-get-model' and other list tools.

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?

Implies usage for filtering models but does not explicitly state when not to use (e.g., for a single model, use dbt-get-model). No mention of prerequisites or context.

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