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 behavioral burden. It does convey a key behavioral trade-off (summary=true produces compact stats and saves context) and implies a read-only operation, but it does not describe the default output shape, count limits, or chart-relative behavior beyond the schema's parameter descriptions.

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?

Two short sentences, front-loaded with the primary purpose and then a practical usage tip. Every word earns its place; 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?

For a low-complexity tool with two optional, fully documented parameters, the description plus schema is nearly sufficient. It lacks an explicit statement of the return structure, but no output schema exists and the OHLCV bar concept makes the expected fields reasonably clear.

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 input schema already describes both parameters completely (100% coverage), so the baseline is 3. The description adds value by explaining why summary=true should be used (compact stats, saves context) and clarifies the all-bars vs summary distinction beyond the schema's wording.

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?

Description starts with a specific verb 'Get' and resource 'OHLCV bar data', scoped to 'the chart'. This clearly identifies the tool's function and distinguishes it from siblings like data_get_indicator or data_get_study_values, which target different data.

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 only usage guidance is internal to the tool: use summary=true for compact stats instead of all bars to save context. It does not explicitly state when this tool should be preferred over sibling data_get_* tools, though the tool name and purpose make that largely inferable.

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