detect_schema_evolution

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

No annotations are provided, so the description carries the full burden. It adds useful behavioral details: 'Walk an ordered list' indicates the order matters, 'Output size: small' sets expectations. However, it does not explicitly state that the tool is read-only or explain what happens if the list is unordered. Moderate disclosure.

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 with the main action. It uses a compact list for detected changes and includes a useful output-size note. No wasted words.

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 tool with one parameter and an output schema provided, the description covers the core purpose, detection types, and use case. It lacks explicit details on return structure, but the output schema fills that gap, making it sufficiently complete.

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 0%, so the description must compensate. It adds the key semantic that source_ids must be 'ordered' and that they are walked sequentially. However, it does not define valid source_id formats or how ordering is determined, leaving some ambiguity.

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's function: 'Walk an ordered list of source_ids and report schema changes over time.' It also enumerates specific detection types (column added, column removed, type changed), making it distinct from sibling tools like detect_pattern or detect_metadata.

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?

Provides a clear use case: 'Useful for monitoring partitioned data (monthly parquets, API version snapshots) for breaking changes.' This gives context for when to use the tool, though it does not explicitly exclude alternatives or mention when not to use it.

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