get_ohlcv

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

Beyond the annotations (readOnlyHint, openWorldHint, destructiveHint), the description discloses crucial behaviors: pagination via next_cursor, split/dividend adjustments for equities vs. unadjusted crypto, and a specific credit cost formula. This adds significant context for expected behavior and side effects.

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 yet information-dense, using five sentences to cover purpose, use case, pagination, adjustment policy, and cost. Every sentence contributes unique value without redundancy or fluff.

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's complexity (7 parameters, output schema present, multiple asset types), the description covers all essential aspects: what it does, when to use it, pagination, adjustment nuances, and cost implications. It references siblings appropriately and works well with the rich 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?

With 100% schema coverage, the schema already documents all parameters. The description adds value by clarifying pagination usage ('pass next_cursor back as cursor') and the credit cost tied to the limit parameter, which enhances understanding of cursor and limit beyond their schema descriptions.

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 retrieves stored end-of-day OHLCV candles for stocks, ETFs, or crypto, with daily or weekly timeframes. It uses a specific verb ('Get') and resource ('OHLCV candles'), and distinguishes itself from siblings like get_summary by its focus on historical candle 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?

Explicitly specifies when to use: 'Use this for exact-return calculations, charts, and backtests after get_summary identifies a setup.' This provides clear context and references the sibling get_summary as a precursor, making the workflow obvious.

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