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

The description goes far beyond basic behavior by detailing data quality constraints and structural quirks. It explains that later restatements are visible instead of silently replacing originals, and that market closure days are absent rather than zero-filled.

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 definition is comprehensive, packing a vast amount of highly valuable technical context into one block. Although slightly lengthy, the information density is extremely high, making the structure highly effective despite not being perfectly concise.

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 complexity of time-series data and the absence of an output schema, the description is maximally complete. It covers the source type, the nature of the data (immutable/restatable), the practical limit (366 days), and the intended use.

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 100%, setting the baseline at 3. The description focuses heavily on output behavior and use cases, adding no significant semantic meaning to the input parameters beyond what the schema already defines.

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 function ('Get what mkts recorded for one symbol on every day in a date range') and immediately distinguishes the data source ('immutable point-in-time archive'). It effectively differentiates itself from siblings like get_price_history.

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?

It provides explicit guidance on the optimal use case ('Use this for backtesting rather than calling get_assets_as_of once per day'), directly contrasting its utility against a known alternative sibling.

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