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

With no annotations, the description carries full burden and does so thoroughly. It discloses the effect is weak but stable (1.3-1.45x), that the response includes a split-half check, and what metric underlies it (Amihud illiquidity over ~62 days). It also warns about a known limitation (per-coin version failed validation).

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 well-structured and front-loaded with the core purpose, followed by an 'IMPORTANT' caveat. Every sentence adds value: the use-case questions, the effect size, the split-half detail, and the warning. No filler or redundancy.

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?

The description fully covers what the tool returns, how the metric is calculated, and what caveats apply. With an output schema also present, an agent can invoke and interpret this tool correctly without further information.

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?

There are zero parameters, so the baseline is 4. The description adds useful semantic context about the output and measurement method beyond the schema, which is more than necessary for a parameterless tool.

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 states exactly what the tool does: it provides a session-level profile of when crypto is cheapest to execute across the coin universe. It includes concrete use-case questions and specifies the output (four UTC session blocks with relative execution-cost numbers). This clearly distinguishes it from siblings like market_snapshot or top_movers.

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 gives explicit 'Use for' examples and an important caveat advising the agent not to invent per-coin 'best hour' data because that version failed validation. It does not mention alternative tools explicitly, but for a zero-parameter analytics tool the usage 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.