optimize_strategy

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

No annotations are provided, so the description must convey behavioral traits. It only states the optimization method (grid search) but does not disclose side effects (e.g., does it modify anything?), computational intensity, or whether it requires pre-fetched data. Critical behavioral context is missing.

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 that directly states the core functionality. It is concise without being overly brief, though it could include a bit more context without sacrificing brevity.

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 (7 parameters, grid search optimization), the description is minimal. It does not explain what the output contains (though output schema exists), potential runtime, or how results are used. This is insufficient for a tool with no annotations.

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 100%, and parameter descriptions in the schema are clear. The description adds no extra meaning beyond the schema; it merely summarizes the action. Baseline score of 3 is appropriate when schema descriptions are complete.

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 purpose: optimizing strategy parameters using VectorBT grid search. The verb 'optimize' and resource 'strategy parameters' are specific, and the method 'VectorBT grid search' distinguishes it from other backtesting or optimization tools in the sibling list.

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 usage for parameter optimization but lacks explicit guidance on when to use this tool versus alternatives like walk_forward_analysis, backtest_signal, or run_ml_strategy_backtest. No 'when not to use' or prerequisite conditions are mentioned.

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