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

Annotations already declare readOnlyHint=true and openWorldHint=false, so the read-only nature is known. The description adds valuable context beyond annotations: it explains the study design (11,440 runs, 10 templates, 20 coins, 70/30 split) and the specific metrics returned (in-sample vs out-of-sample Sharpe, haircut, rank correlation). This is comprehensive and consistent.

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 two sentences long: the first sets the context (study size and design), the second lists the output (headline stats and picks with specific metrics). Every sentence is necessary and informative, with 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 has only two optional simple parameters and no output schema, the description fully covers the return value structure (headline stats, tuned picks, Sharpe metrics, rank correlation). No additional context is needed for an agent to understand what will be returned.

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 each parameter has a description. The description supplements the schema by providing concrete examples (e.g., 'btc' for coin, 'super_trend' for template) and clarifying that omitting either parameter returns all values. This adds practical value beyond the 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 tool queries a specific open curve-fitting study with 11,440 runs, returns overfitting stats and tuned picks, and can filter by optional template and/or coin. This directly distinguishes it from sibling tools like check_overfitting (which likely checks a single strategy) or compare_strategies.

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 retrieving overfitting stats and filtered picks but does not explicitly state when to use this tool versus alternatives (e.g., when to use check_overfitting instead). The context of 'query our open study' suggests it's for data retrieval, but no when-not-to-use guidance is provided.

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