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

With readOnlyHint/openWorldHint already covering the safety profile, the description goes further: it explains the live-vs-cache fallback, the presence of a coverage_warning, that volume must not be inferred in cache mode, and that total_count is authoritative but omitted when issue/sub_product filters are active. These are non-obvious operational traits that materially affect interpretation. It stops short of covering pagination mechanics or rate limits.

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?

Well front-loaded: purpose first, then usage, then coverage/behavioral caveats. The taxonomy and company-response value lists are long but genuinely useful for constructing valid filters; a couple of enumerations could be tightened without loss.

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?

With 13 optional params, no output schema, and open-world behavior, the description compensates fully — it explains the return surface (total_count vs results, source, coverage_warning), filter interactions, and fallback semantics. An agent has everything needed to call and interpret it 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?

Schema coverage is 100%, so baseline is 3, but the description adds real meaning: it enumerates the exact product taxonomy values and clarifies that company matching is word-based rather than arbitrary-substring. It also warns that issue/sub_product are applied post-upstream (changing what total_count means), which the schema does not convey.

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 opens with a specific verb+resource ('Returns consumer complaints filed with the CFPB') and defines the record granularity ('Each record is one filing against a bank, credit reporting agency...'). It names the concrete fields returned (company response status, timeliness flag, narrative), which clearly separates it from siblings like get_enforcement_actions.

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 enumerates explicit use cases ('complaint volume against a specific company, top issues at a credit reporting agency, regional complaint patterns, untimely responses...'), including forward-looking use as an enforcement indicator. It also names an alternative and the condition to pair with it (get_enforcement_actions with source-specific params), which is exactly the when/when-with guidance the dimension rewards.

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