Complaint counts and percentages per US state, with optional product filter and date range. Useful for state-level financial-consumer risk maps.
| Name | Required | Description | Default |
|---|---|---|---|
| product | No | Optional product filter | |
| date_received_max | No | YYYY-MM-DD upper bound | |
| date_received_min | No | YYYY-MM-DD lower bound |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already establish that the tool is read-only, idempotent, and non-destructive. The description adds useful behavioral context by specifying that output is state-level counts and percentages and that product and date-range filtering are available. It does not describe exact response shape or how zero-complaint states are handled, but the key behavioral profile is clear.
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 a single, focused sentence that front-loads the core result type and scope, then adds filter options and a use case. There is no redundant or filler content.
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?
For a simple read-only aggregation tool, the description plus schema cover the essential call decision: output type, optional filters, and intended use case. The lack of an output schema means exact return fields are not specified, but this is a minor gap given the simple output described.
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 input schema already documents all three optional parameters with 100% coverage, so the baseline is 3. The description loosely restates these as 'product filter and date range' without adding format details or value constraints beyond what the schema provides.
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 identifies the resource and output: 'Complaint counts and percentages per US state' with optional filters. However, it does not explicitly distinguish itself from sibling CFPB tools such as cfpb_complaint_trends or cfpb_complaint_aggregations, so sibling differentiation is left implicit.
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 a clear use case ('state-level financial-consumer risk maps') and implies geographic aggregation, but it does not state when to prefer another CFPB tool or when not to use this tool. The usage context is present but no exclusions or alternatives are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.
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.
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.
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.