data_get_ohlcv

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

No annotations are provided, so the description carries full responsibility for behavioral disclosure. It does reveal that summary=true returns 'compact stats instead of all bars' and that using it 'saves context,' which is a significant behavioral trait. However, it does not describe other behaviors such as return format, potential errors, or the fact that it might be a read-only operation. This is a moderate level of transparency.

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 exceptionally concise—one sentence plus a targeted tip. It front-loads the core purpose and immediately follows with actionable guidance. No wasted words or irrelevant details.

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 tool with two well-documented parameters and no output schema, the description is mostly complete. It covers what the tool does and offers advice on a key parameter. However, it could be more complete by mentioning what the output looks like (e.g., a list of OHLCV bars) or clarifying that it reads from the currently active chart, but these are somewhat implied. Good overall.

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?

Both parameters are already fully described in the schema (count with range and default, summary with details about what it returns). The description adds practical value by emphasizing the context-saving benefit of summary=true, which goes beyond the schema's 'much smaller output' wording. This enhances the agent's ability to choose the right parameter mode.

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 tool's purpose with a specific verb and resource: 'Get OHLCV bar data from the chart.' This distinguishes it from sibling data tools like data_get_indicator or data_get_trades, as it specifically targets OHLCV bars. No ambiguity.

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 useful context on how to use the tool, specifically recommending summary=true for compact stats to save context. However, it does not explicitly mention when to use this tool versus alternative data retrieval tools, though the purpose makes the primary use case clear. It lacks exclusions or alternative tool references.

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