incident-context

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

No annotations are provided, so the description must convey behavioral traits. It mentions the tool returns a 'deep-dive' of definition, test failures, and DQ checks, but does not clarify if it is read-only, its performance implications, or any side effects. The agent cannot infer whether multiple calls are safe or if authentication is needed.

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 two sentences long, front-loading the core functionality and purpose. Every sentence adds value without redundancy. It is concise and well-structured for quick understanding.

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 three parameters, the description provides a high-level overview of the return value (definition, failures, checks) but lacks specifics on structure or format. For a complex tool combining multiple data sources, this is adequate but not fully sufficient for an agent to predict the output shape without additional context.

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 input schema covers 2 of 3 parameters with descriptions (modelName and sourceFqn), covering 67% of parameters. The description adds little beyond this: it restates the purpose but does not detail parameter usage or constraints (e.g., exclusivity of modelName/sourceFqn or the meaning of sinceHours). Baseline 3 is appropriate given moderate 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 provides a 'single asset deep-dive' including 'dbt definition + recent test failures + DQ checks'. The phrase 'Designed to anchor an LLM-driven incident triage' further reinforces its specific role. This is distinct from sibling tools like dbt-get-model or dq-failed-checks-by-dataset, which focus on narrower aspects.

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 implies the tool is for incident triage but does not explicitly state when to use it versus alternatives or when not to use it. Sibling tools exist for more granular concerns (e.g., dbt-get-model for pure definition, dq-failed-checks-by-dataset for DQ alone), but the description offers no comparative guidance.

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