fact_history

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

With no annotations, the description carries the full burden and discloses key behaviors: ordering by event_time, inclusion of superseded versions, and automatic entity normalization (Apple Inc./ISIN/AAPL). It omits limit semantics and error behavior, but the disclosed traits are substantial.

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?

Three sentences with no filler: the first states the core action, the second gives a use case, and the third adds behavioral details. Information is front-loaded and every sentence earns its place.

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Given no output schema, the description covers the main expectations: what is returned (versions), ordering, superseded handling, and normalization. It does not explicitly describe the response structure or pagination, but for a tool of this complexity it is reasonably complete.

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 descriptions cover ticker and metric, and the description adds entity normalization and time ordering context that clarifies parameter usage. However, the 'limit' parameter remains undocumented in both schema and description, preventing a higher score.

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 uses a specific verb ('Return') plus resource ('every recorded version of a structured fact') and distinct behavior ('ordered by event_time'), clearly distinguishing it from sibling tools like recall or remember. It leaves no ambiguity about what the tool does.

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?

Provides clear context with a concrete example ('show me how AAPL EPS evolved over the last four quarters') indicating it is ideal for time-series views. It does not explicitly name alternatives or when not to use it, but the context is strong enough for an agent to select it appropriately.

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