get_price_history

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, so the safety profile is covered. The description adds that results bundle candles with an aggregate summary, but says nothing about data source, latency, or rate limits; for an open-world market-data read, this is adequate but not rich.

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?

A single front-loaded sentence that leads with the primary payload (OHLCV candles) and then the secondary summary fields. Every clause carries information; nothing is padded.

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?

With a full output schema present, the description need not explain return values, and it correctly focuses on what data comes back. Combined with 100% schema coverage, an agent has nearly everything needed; only the choice against get_quotes is unstated.

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 100%, so the schema fully documents all seven parameters, including the enum, defaults, and the period/start_date override relationship. The description adds no parameter detail beyond that, so the baseline 3 applies.

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 names the concrete output (OHLCV candles plus change, % change, high/low, and average volume) and scopes it to a period, so an agent knows exactly what data it returns. It does not, however, explicitly differentiate itself from the sibling get_quotes tool, which is the nearest conceptual neighbor.

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?

There is no statement of when to use this tool versus alternatives such as get_quotes, nor any prerequisites or exclusions. 'For a period' hints at historical rather than real-time data, but the routing decision is left entirely to inference.

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