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

Annotations already cover the safety profile (readOnlyHint=true, idempotentHint=true, destructiveHint=false), so the bar for added value is lower. The description adds meaningful behavioral context beyond annotations: the $0.02 USD per-call cost, the external DataHub dependency, the asset classification taxonomy (charts/dashboards/pipelines), and the three-verdict output scheme. No contradiction with annotations.

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 sentences with zero waste: the cost is front-loaded (important for an agent deciding whether to spend), followed by the value question, then the mechanism. Every clause earns its place, and the question format is an efficient scaffold for the agent's intent.

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?

The description names the output verdicts (BLOCK/REVIEW/SAFE) and the classification categories (charts/dashboards/pipelines), which gives a usable picture of the return despite no output schema. The payment two-step flow lives in the schema, so the description need not repeat it. Minor gap: the exact response structure beyond verdict types is unspecified, but annotations plus schema cover the remaining operational needs.

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?

Schema coverage is 100%, so the baseline is 3, but the datasetUrn schema description ('Path parameter datasetUrn') is tautological and adds no meaning. The description compensates by establishing that datasetUrn is the table under consideration in DataHub lineage, which gives real semantic weight. The payment parameter is already well-documented in the schema with the two-step retry flow.

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 states a specific verb ('Walks'), a specific resource (DataHub downstream lineage), and a concrete outcome (BLOCK/REVIEW/SAFE verdict). The quoted question 'what breaks if I drop/change this table?' crisply frames the use case. None of the sibling tools (deal_tracker, token_security, wallet_analysis, etc.) do data lineage, so it is clearly distinguished.

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 embeds a clear trigger condition via the question 'what breaks if I drop/change this table?' and the blast-radius framing tells an agent exactly when this tool applies. It does not explicitly name alternatives or exclusion conditions, but no sibling tool overlaps with lineage analysis, so explicit routing is unnecessary.

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