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

With no annotations, the description carries the full behavioral burden, and it delivers: it explains the structural near-zero revert rates for non-router venues, the spam-sender inflation issue, the filtering behavior of comparable_only, and the row-level fields the agent should quote. This goes well beyond surface-level tool description.

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 front-loaded with a precise definition, then uses bolded warning text for the most critical caveat, and closes with concrete field-level guidance. Every sentence earns its place, and the structure makes the ranking warning impossible to miss.

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 is thorough for a read-only metrics tool: it covers the core metric, the comparability trap, the spam-inflation nuance, and the fields to quote. It does not describe the full return shape or ordering, but the provided field names and defaults give an agent enough context to invoke and interpret the tool correctly.

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?

The description adds meaningful semantics for comparable_only, explaining its default behavior and why it matters. However, chain, project, and limit are left to their titles and defaults; with 0% schema description coverage, the description does not fully explain those parameters, though they are reasonably inferable.

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 defines the tool's purpose: measuring the share of a venue's routing transactions that fail on-chain, per chain. It also distinguishes the metric from slippage by framing reliability and price as different questions, so an agent understands exactly what this tool provides.

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 usage rules: do not rank across execution models, use comparable_only for valid comparisons, prefer user_revert_rate_pct when present, and quote execution_model and revert_comparable flags. It even explains why batch-auction/intent venues would otherwise be misleadingly ranked, leaving little room for misuse.

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