pbi_model_lineage

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

No annotations are provided, so the description must carry the full burden. It indicates it returns a graph (likely read-only), but does not disclose potential side effects, authentication needs, error behavior, or whether the operation is safe. Only minimal behavioral context is given.

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?

Two concise sentences with no wasted words. The first sentence defines the tool's output (dependency graph), and the second explains the essential input. It is appropriately sized and front-loaded.

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 (one parameter, no output schema), the description covers the core functionality. However, it omits details about the output format (e.g., JSON structure) and ignores potential issues like invalid measure names. Adequate but incomplete.

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 description adds meaning to the 'measure' parameter by stating it should be passed for the dependency graph. With 0% schema description coverage, this explanation is helpful but lacks details on format, optionality (though schema shows default null), and constraints.

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 returns a dependency graph for a given measure, specifying 'who references whom' and 'deps + dependents'. This distinguishes it from sibling tools which focus on backups, CRUD operations on measures, or model settings.

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 tells the user to pass a measure to get its dependencies and dependents. However, it does not explicitly mention when not to use this tool or suggest alternatives, but the context is clear enough for an agent to decide.

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