list_fees_revenue

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

No annotations provided, but the description discloses the return format, the subtle field naming behavior when data_type changes, and the distinction between fees and revenue. Does not address destructive actions or auth needs, largely unnecessary for a read-like operation.

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 verbose and includes extensive return field details. While well-structured with Args/Returns sections, it could be more concise without losing clarity.

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?

No output schema, but the description provides complete return format details (summary and protocols fields). Covers parameter semantics and behavioral nuances thoroughly, making the tool fully understandable for invocation.

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?

Input schema has 0% description coverage, but the description compensates fully: explains each parameter's role, enum values meaning, and the note about per-window field names referring to the chosen metric. Adds significant value beyond the schema.

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 lists protocols ranked by fees or revenue, with specific use cases and differentiation between fees and revenue. It distinguishes from sibling tools like list_protocols and list_dex_volumes by focusing on fee/revenue metrics.

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 explicit examples of when to use (e.g., 'which protocols make the most money?') and explains the data_type parameter with practical guidance. Does not explicitly mention when not to use or alternatives, but the context is sufficient.

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