get_schema_drift

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

Annotations already declare readOnlyHint=true (safe read). The description adds behavioral detail: 'Pure in-memory comparison — no re-reading source files,' and discloses the output assessment format (identical/additive/breaking). 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?

The description is concise and front-loaded, stating the purpose first, then expanding to detection scope, behavior, and output categories in four sentences with no redundant 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 two-parameter, no-output-schema tool, the description is complete: it covers comparison dimensions, in-memory behavior, use case, and possible assessment results. No critical details are missing for effective use.

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?

Input schema provides 100% coverage with descriptions for dataset_a (baseline) and dataset_b (comparison target). The description itself adds no additional parameter semantics, so the baseline score of 3 applies.

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 'Compare schema (columns, types, nullability) between two indexed datasets' with a specific verb and resource. It distinguishes from siblings like describe_dataset (single dataset) and get_schema_impact (query impact), and clearly lists detection capabilities and assessment categories.

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 context: 'Useful for detecting schema changes between dataset versions.' However, it does not explicitly name alternatives or state when not to use this tool, though the context is unambiguous enough.

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