apply_optimization

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

Annotations indicate mutating but not destructive. The description adds important context: non-interactive (--yes), return shape {result_file, strategy_id, output}, progress reporting, execution timeout. No contradictions 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?

Four sentences, front-loaded with purpose, no wasted words. Each sentence adds unique value: prerequisite, execution mode, return, follow-up.

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 2 params and an output schema (mentioned return fields), the description covers prerequisites, execution behavior, and follow-up. No gaps given the simplicity.

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?

The schema has 0% description coverage, but the description fully explains both params: result_file is the saved_path from run_optimize, and strategy_id is implied from the prerequisite. This adds meaning beyond the raw 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 clearly states the action: 'Apply an optimization result file to a strategy', specifies the naming convention for the output ('saves `<strategy_id>_optimized`'), and distinguishes this tool from siblings like run_optimize which produce the result file.

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?

Prerequisite is explicitly given: 'run_optimize(save=true) — result_file is its saved_path'. It also notes non-interactive execution and suggests follow-up with generating Pine Script. No explicit when-not-to-use, but the context is clear.

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