get_stable_liquidity

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

Without annotations, the description fully explains behavior: computation via walking the order book, meaning of buy_pct/sell_pct, null values for insufficient depth, and the fixed sizes, providing comprehensive transparency.

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 sentences plus an example, front-loaded with key information, no wasted words, efficient and clear.

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 no parameters and no output schema, the description covers all important aspects: markets, sizes, value interpretation, and even an example query, making it sufficiently complete.

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?

No parameters exist; schema coverage is trivially 100%. The description adds substantial meaning about the tool's behavior, going beyond schema by explaining output interpretation, which is helpful.

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 it returns spread and market-impact slippage for stablecoin markets on Buda.com, specifying exact markets, fixed sizes, and interpretation of values, distinguishing it from sibling tools like get_spread.

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?

Provides context with example query, but lacks explicit when-to-use vs alternatives or when-not-to-use guidance, though the specificity makes usage clear.

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