data_get_study_values

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

With no annotations, the description carries the full burden and delivers key behavioral traits: 'SIDE EFFECTS: None (Read-only)' and 'LIMITATIONS: Indicators must be visible on chart.' It does not describe authentication, rate limits, or error behavior if indicators are hidden, but for a read-only extraction tool this is solid coverage.

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 and front-loaded: purpose first, then labeled sections for when to use, side effects, and limitations. Every sentence earns its place with no redundant or filler text.

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?

For a simple zero-parameter, read-only extraction tool with no output schema, the description covers purpose, when to use, side effects, and a precondition. It stops short of describing the return structure (e.g., how indicator names map to values), which would be helpful given the lack of an output schema.

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 zero parameters, so parameter semantics baseline is 4. The description adds no parameter-specific information because there are no parameters to document.

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 ('Extract') and resource ('real-time numerical values of all visible indicators from the chart Data Window'), and lists examples (RSI, MACD, EMAs, custom plot() outputs). The phrase 'all visible indicators' implicitly distinguishes it from the singular sibling data_get_indicator, so an agent can tell them apart.

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?

An explicit 'WHEN TO USE' section tells the agent to call this when reading current indicator values directly from TradingView without recomputing in Node.js. However, it does not name an alternative tool or state when not to use it, so it falls short of the full when/when-not/alternatives standard.

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