impact_analysis

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

Beyond the readOnly/openWorld annotations, the description discloses non-obvious traits: `name` is exact and case-sensitive, workbooks are derived from downstream sheets/dashboards because the flat downstreamWorkbooks edge is unreliable, and an empty result on a site without Data Management signals unindexed transitive lineage. These are exactly the operational caveats annotations cannot carry.

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?

Front-loaded with the core capability, then the routing contrast, then operational caveats — a deliberate and useful ordering. It runs slightly long with the parenthetical asides, but every sentence carries distinct information rather than restating the schema.

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?

Although an output schema exists (so return values need not be explained), the description voluntarily enumerates the return shape (dependent fields, sheets, dashboards, workbooks, de-duplicated owners) and covers the critical failure mode. Nothing an agent needs to invoke or interpret this tool is missing.

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?

With one parameter at 0% schema description coverage, the description must compensate, and it does: it tells the agent the argument accepts a column, field, or table and must match exactly and case-sensitively. It could still be clearer about naming conventions for non-column targets, but the key semantic is covered.

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?

States a specific verb+resource ('transitive MULTI-HOP downstream impact of a column, field, or table') and immediately distinguishes itself from the sibling `where_used` (one-hop core lineage). An agent can select between the two without opening either schema.

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?

Gives an explicit use case ('what breaks if I change/drop this?'), names the alternative `where_used` and the exact condition that selects it, plus a documented fallback when the tool returns nothing. Nothing is left to inference.

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