data_get_study_values

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

No annotations are provided, so the description carries the full burden. It discloses that the tool reads current values and only for visible studies, which is useful. But it does not describe the output format, whether values are last-bar snapshots or series, or how multiple studies like Bollinger Bands are represented.

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?

A single, focused sentence with a helpful parenthetical list of study types. Every word contributes meaning, with no redundancy or filler.

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?

The tool is simple with no parameters, but it also has no output schema. The description names the data source and scope but remains vague about the returned structure, which matters for an agent consuming the result programmatically. It is adequate but leaves room for clarification.

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?

The tool has 0 parameters and the schema is fully empty, so there is no parameter information that the description needs to add. The baseline of 4 applies.

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 a specific verb ('Get'), a specific resource ('current indicator values from the data window'), and a precise scope ('all visible studies'). It also gives concrete examples (RSI, MACD, Bollinger Bands, EMAs, custom indicators), which makes the tool's function unmistakable and distinguishes it from sibling data_get_* tools.

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 phrase 'all visible studies' gives clear context for when this tool is appropriate. However, it does not explicitly state when to prefer a sibling tool like data_get_indicator for a single indicator, nor does it mention exclusions.

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