run_optimize

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

The description discloses important behavioral traits not covered by annotations: it is long-running (up to 600s), reports progress to capable clients, and has a save option that persists data (implies writing). Annotations indicate non-read-only and non-destructive, which aligns, but the description adds specific details about execution duration and progress reporting.

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 with three sentences, each adding value. It front-loads the core purpose, then explains save behavior and runtime characteristics. No unnecessary 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 has 5 parameters with no schema descriptions, the description covers main functionality, save parameter details, and runtime behavior. It doesn't explain return values (output schema exists) or 'trials' parameter, but overall it provides sufficient context for an agent to use the tool correctly.

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 0%, so the description must compensate. It adds meaning for 'metric' (default sharpe_ratio) and 'save' (purpose and default), but provides no extra guidance for 'strategy_id', 'trials', or 'symbol' beyond their names. This partial compensation yields a score of 3.

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 ('optimize strategy parameters with Optuna'), the resource (strategy parameters for a given symbol), and the default metric. It differentiates from sibling 'apply_optimization' by implying that this tool generates the optimization result which can then be applied.

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 explains the save parameter's role and how it relates to 'apply_optimization', providing context on when to persist results. It also mentions the long-running nature and timeout, hinting at appropriate usage. However, it lacks explicit guidance on when not to use this tool or contrasts with other backtesting siblings.

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