Industry-level summary financials. Returns year-by-year aggregates across all FDIC-insured institutions, optionally filtered to a single state. Useful for macro banking-sector analysis.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Number of years (default 20) | |
| state | No | Two-letter state code (omit for national) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover read-only, idempotent, non-destructive behavior, so the bar is lower. The description adds meaningful behavioral context: it returns aggregates rather than raw institution records, is year-by-year, covers all insured institutions, and can be optionally filtered by state. This goes beyond the annotations and clarifies the tool's granularity.
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?
Three short, front-loaded sentences state the scope, output, optional filter, and use case without any wasted words. The description is compact yet informative and easy to parse quickly.
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 tool with only two optional parameters, the description covers purpose, data granularity, filtering, and intended use case. It does not enumerate the specific financial metrics returned, but the 'summary financials' and 'macro analysis' framing is sufficient for an agent to select and invoke 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 description coverage is 100%, so the schema already documents both limit and state. The description reinforces that the state filter is optional and that results are generated at the industry level, but it does not add substantive parameter semantics beyond what the schema provides. Baseline 3 is appropriate.
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 states a specific verb ('Returns') and resource ('year-by-year aggregates across all FDIC-insured institutions'), and clearly distinguishes itself from institution-level siblings by calling out 'Industry-level summary financials' and 'macro banking-sector analysis.' An agent can immediately tell this is not another FDIC lookup or institution-level tool.
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 provides clear context: use it for macro banking-sector analysis, not for institution-specific questions. It does not explicitly name alternatives or exclusions, but the 'industry-level' framing and optional single-state filter imply when to use it relative to more granular FDIC tools.
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.