apply_script_patch

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 discloses atomicity, exact-text matching preconditions, dry-run capability, idempotency, verification, and rollback—far more than typical mutation tools. While it doesn't detail failure semantics or permissions, the disclosed traits give an agent a strong sense of the tool's safety profile and side effects.

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 a single sentence with dense, relevant information. Every word contributes: atomicity, exact-text edits, preconditions, and feature list. It is front-loaded with the core action and avoids redundant or filler text.

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?

This is a complex tool (6 parameters, nested objects, no output schema, no annotations), but the description only provides a high-level summary. It fails to mention critical operational details such as the requirement for an idempotency_key during live mutation, how dry_run interacts with other parameters, or what verification/rollback entail. An agent would need the full schema and inference to use this correctly, making the description incomplete for a tool of this complexity.

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 67%, so the schema does most of the work. The description adds high-level context by connecting source-hash, match-count, and dry-run to the respective parameters (expected_source_hash, expected_count, dry_run), which helps interpretation. However, it does not clarify the distinction between `path`+`edits` and the `changes` array, nor the behavior of `replace_all` vs `expected_count`. This is acceptable but not exceptional.

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 applies exact-text script edits with specific preconditions (source-hash, match-count) and features (dry-run, idempotency, verification, rollback). It uses a specific verb and resource, making the purpose unambiguous. However, it does not explicitly differentiate from sibling tools like generate_change_plan or verify_change, so it misses the top score.

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?

There is no guidance on when to use this tool versus alternatives such as generate_change_plan, verify_change, or rollback_change. The description implies usage through its feature list but does not state scenarios, prerequisites, or exclusions. This is a significant gap for a tool with many siblings.

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