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

With no annotations provided, the description carries the full burden of behavioral disclosure. It covers ordering ('oldest first'), the default for ending_day ('defaults to the last complete day'), and the row cap behavior ('markets times window_days must stay inside the row cap; the response states the window it used'). This is transparent about limits and defaults, though it does not describe the return schema or error handling – but the output schema likely covers return structure.

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 compact (three sentences) and front-loaded with the core purpose and constraints. Every sentence contributes either the scope, the use-case context, or the cap/default behavior. No filler or redundancy.

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 and an output schema, the description covers the essential operational aspects: what the data looks like, the ordering, defaults, and the cap. It references list_fields for columns, which ties to sibling tools. It lacks explicit 'when not to use' guidance, but that is minor given the clarity of purpose and the presence of siblings that naturally differentiate.

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%, so each parameter already has a description. The description adds value by explaining the relationship between coins and window_days via the row cap, and by noting that ending_day defaults to the last complete day (which the schema only implies via default null). It also points to list_fields for column choices, giving context beyond the schema. This is a solid enhancement beyond the structured definitions.

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 states a specific verb and resource: 'The daily series for one market or a few (max 10): one row per market per day over a trailing window, oldest first.' It clearly distinguishes from cross-sectional or live tools by emphasizing time-series structure and the max-10 constraint. The purpose is unambiguous and immediately scopes the tool.

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 provides usage context by stating the questions it answers ('is this move backed by real flow' and 'how has positioning built up'), which implies when an agent would want it. It also mentions the row cap constraint, which helps the agent plan queries. It does not explicitly name alternatives or exclusions, but the guidance is clear enough for most use cases.

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