get_grid_ai_strategy

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

No annotations exist, so the description bears the transparency burden. It discloses the source/type of the data (AI-recommended, exchange-provided) but not the return structure's full meaning, possible errors, or other behavioral details. The 'get' naming implies a read-only operation, so this is acceptable but not exhaustive.

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, dense sentences. The first states what the tool returns, and the second adds actionable usage guidance. No redundancy, no filler.

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 getter with two plain parameters, the description explains the essence and even names the output fields (top/bottom/row). It does not belt-and-ribbon around symbol formats or error cases, but the core use case is well covered.

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%, and the description only mentions 'a pair' without clarifying the expected formats for 'base' and 'quote' or the relationship between them. The agent must infer that base/quote are market symbols, which is a notable gap given the lack of schema documentation.

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 explicitly names the action ('get'), the resource ('Pionex's own AI-recommended grid parameters: top/bottom/row'), and the scope ('for a pair'). It clearly differentiates this from ordinary grid creation by emphasizing these are exchange-provided numbers rather than user-invented bounds.

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?

It gives a clear usage directive: 'Use these exchange-provided numbers instead of inventing grid bounds,' which tells the agent when this tool should be called. It does not explicitly name alternative tools or edge cases, but the context is clear enough.

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