Identify Schema and Content Drift

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

Annotations already establish read-only, idempotent, and non-destructive behavior. The description adds meaningful behavioral context beyond this: results are ranked with structural changes first, a minimum transition count can be required, and evidence is pipeline-computed. This helps the agent understand what kind of output to expect and how evidence differs from freshness inferences.

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 with no filler. The core purpose and ranking behavior are front-loaded, the optional parameter is mentioned naturally, and the closing clause about pipeline-computed evidence prevents a common misconception. Every sentence contributes useful information.

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?

For a read-only tool with an output schema and full parameter documentation, this description is nearly complete. It explains the ranking, the optional filter, and the nature of the evidence. It does not detail edge cases like empty results or open-world behavior, but the annotations and output schema cover much of that context, so only minor gaps remain.

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 description coverage is 100%, so the schema already fully documents both parameters. The description adds a small clarification for min_change_count by calling it 'structural transitions', but this largely mirrors the schema's 'structural fingerprint or column-count transitions'. There is no substantial extra semantic value beyond what the schema provides, so the baseline score of 3 is appropriate.

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 datasets with published structural or record-count drift evidence, ranked with structural changes first. It names a specific verb, resource, and ordering behavior, and this distinguishes it from related sibling tools like find_stale or find_anomalies by focusing on drift evidence rather than general freshness.

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 provides clear usage context: it should be used when pipeline-computed drift evidence is needed, and agents should not infer drift from freshness alone. It does not explicitly name alternative tools or exclusion conditions, but the guidance is specific enough to prevent the most likely misuse.

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