National Flood Insurance Program claim history aggregated by zip, county, or state. Useful for insurance brokers and homebuyers assessing prior loss patterns.
| Name | Required | Description | Default |
|---|---|---|---|
| zip | No | Five-digit zip code. | |
| limit | No | Max rows (default 200). | |
| state | No | Two-letter state code. | |
| county | No | FEMA county code. | |
| end_year | No | Latest year of loss. | |
| start_year | No | Earliest year of loss. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only, idempotent, non-destructive behavior. The description adds that results are aggregated by geography and tailored to loss-pattern assessment, but it does not disclose output size, pagination/default limits, date coverage, or what happens when no geography filter is provided. Given the rich annotations, the added behavioral context is modest but acceptable.
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?
Two short sentences, front-loaded with the core function and followed by a useful audience statement. There is no redundancy or unnecessary detail.
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 read-only aggregation tool, the description plus 100% schema coverage and safety annotations provide a usable picture. It is slightly incomplete because it never mentions output fields or whether a geography filter is expected, and there is no output schema to fill that gap.
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 each parameter is already documented including zip, county, state, years, and limit. The description only repeats the geographic aggregation concept and adds no extra meaning beyond the schema.
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 NFIP flood claim history aggregated by zip, county, or state, which is specific enough to differentiate it from sibling tools like flood_zone_lookup. It lacks an explicit action verb such as 'get' or 'list' and does not name sibling distinctions, so it stops just short of a 5.
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 states a concrete audience and scenario: insurance brokers and homebuyers assessing prior loss patterns. It does not explicitly say when not to use it or point to alternatives, but the intended use context is clear.
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.