Get OHLCV

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

No annotations are provided, so the description must disclose behavioral traits. It only mentions 'normalized' and 'historical', but lacks details on data source, rate limits, error handling, or the meaning of 'normalized'. This is insufficient for an agent to predict behavior.

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 a single, front-loaded sentence with no wasted words. However, it is so concise that it omits critical information, which slightly reduces the score from a perfect 5.

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 the tool has 4 parameters (2 required) and no output schema, the description is too brief. It does not explain return format, error scenarios, or parameter details. This leaves significant gaps for an AI agent to use the tool correctly.

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 description coverage is 0%, so the description should compensate. It does not explain any parameter beyond the field names. For example, 'symbol' format, 'interval' units, 'limit' meaning, or 'provider' role are left ambiguous. No added value over schema.

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 returns normalized historical OHLCV bars for a symbol. The verb 'Get' and resource 'OHLCV bars' are specific, and it distinguishes from sibling tools like get_quote (current price) and calculate_indicators (derived 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 description provides no guidance on when to use this tool versus alternatives like get_quote for current data or search_symbols for symbol lookup. There is no mention of constraints, prerequisites, or context for usage.

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