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cfpb_complaint_trends

Read-onlyIdempotent

Time-series trends of complaint volume. lens=overview shows total complaints over time; lens=product shows by product; lens=company shows by company; lens=issue shows by issue. Interval can be month, quarter, or year.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
lensYesTrend dimension
productNoFilter to a specific product
sub_lensNoOptional sub-dimension
trend_depthNoTop N to track (default 5)
trend_intervalNoTime bucket size
date_received_maxNoYYYY-MM-DD upper bound
date_received_minNoYYYY-MM-DD lower bound

TDQS

A3.7/5.0
Behavior4/5

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

Annotations already establish read-only, idempotent, non-destructive behavior. The description adds meaningful behavioral context beyond the schema by explaining what each lens produces and what interval granularities are available. It does not discuss output shape or aggregation details, but the annotations lower the bar here.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is three concise sentences: the first front-loads the purpose, and the next two compactly explain the lens values and interval options. Every sentence contributes useful information with no wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a read-only tool with seven parameters, the description plus a fully described schema is mostly sufficient. It explains the central lens and interval semantics, and parameter descriptions cover the rest. However, it does not describe return-value structure or how product/sub_lens interact with lenses, leaving minor gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the baseline is 3. The description adds genuine meaning to the lens parameter by specifying what each value shows, and clarifies trend_interval choices. It does not elaborate on date bounds, product, or sub_lens, but those already have self-explanatory schema descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool returns time-series trends of complaint volume and enumerates the lens options (overview, product, company, issue). It does not explicitly contrast itself with sibling tools like cfpb_complaint_aggregations or cfpb_state_complaints, so it misses full sibling differentiation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool instead of the other CFPB complaint tools, and it does not mention alternatives or exclusions. The time-series framing implies temporal analysis, but there is no explicit decision guidance.

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

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TDQS

B3.2/5.0
Disambiguation2/5

Many tools overlap heavily across domains: caselaw_search vs court_case_search vs court_opinion_search, caselaw_citation_lookup vs court_citation_resolver, and a cluster of company due-diligence tools (company_trust_check, counterparty_risk_score, entity_dossier, issuer_diligence_dossier, kyb_aml_evidence_case_file) that all screen a company for sanctions/risk/standing. With 290 tools, an agent will frequently face multiple equally plausible choices for the same user intent.

Naming Consistency3/5

The vast majority of tools follow a clean domain-prefix + snake_case pattern (census_, eia_, fmcsa_, npi_, cfpb_, etc.), but there are notable exceptions: entity_resolve and resolve_entity are reversed duplicates, reg_search (Federal Register) sits next to reg_cfr_search (CFR) with confusingly similar names, and carrier_monitor_recheck deviates from the carrier_vetting_* family.

Tool Count1/5

290 tools is an extreme count under any rubric, far exceeding even the 50+ threshold for the lowest score. While the group-filtering mechanism and meta-tools like list_tool_groups and search_available_datasets mitigate the practical burden, the raw surface is still massively oversized for an agent to select from accurately and efficiently.

Completeness4/5

For a read-only data-aggregation server, coverage is remarkably comprehensive across 59 domains, and generic fallbacks like cdc_dataset_query, eia_series_lookup, fred_observations, and bls_series prevent most dead ends. Minor gaps exist (a single GitHub tool, demo-only property_lookup coverage, no write/update operations anywhere), but the stated data-access purpose is well served.

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