apply_frankability_edit_ops

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

With no annotations, the description fully discloses behavior: validates every op, snapshots files, applies, verifies via frank.toJSON, and rolls back on regression. It also mentions the return payload includes applied/skipped/rolledBack/baseline/finalState.

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 fairly long but structured with a summary line followed by details. It's front-loaded and every section adds value, though it could be tightened. 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?

Given the tool's complexity, the description covers input format, validation/apply/verify/rollback behavior, and return value. It references prescribe_frankability_fixes for op kinds, which is acceptable since that's a sibling tool. No output schema was provided, but the return fields are described.

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 explains ops_json format in detail (either wrapped in {'ops': [...]} or bare array) and clarifies symbols_dir is the absolute path to the symbols/ directory. This adds meaning beyond the bare schema.

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 opens with a specific verb+resource: 'Apply LLM-generated edit ops to a Symbols project with verify-or-rollback.' This clearly distinguishes it from siblings like prescribe_frankability_fixes (which generates ops) and rollback_frankability (which reverts).

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 implies when to use it: when you have LLM-generated edit ops to apply. It references prescribe_frankability_fixes for the op kinds, but doesn't explicitly state exclusions or alternatives like audit_and_fix_frankability. Still, context is clear.

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