Multi-year FEMA disaster summary for a location. Buckets declarations by incident type and year so insurance brokers and realtors can assess cumulative risk on the same address used in property_lookup.
| Name | Required | Description | Default |
|---|---|---|---|
| state | Yes | Two-letter state code. | |
| years | No | Lookback window in years (default 10). | |
| county | No | County name. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and idempotentHint=true, and the description does not contradict them. It adds useful behavioral context by describing multi-year scope and output grouping by incident type and year. It does not disclose output shape, pagination, or data coverage nuances, but the safety profile is already handled by annotations.
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 two tight sentences with the core purpose front-loaded, followed by the aggregation behavior and the target audience/workflow. Every clause contributes useful information with no filler or redundancy.
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 annotations covering read-only/idempotent behavior and the schema covering parameters, the description provides enough grouping semantics ('by incident type and year') for an agent to know what to expect. The lack of an output schema is partially mitigated by that grouping statement, though the address-vs-state/county reference is a minor source of ambiguity.
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 carries the parameter definitions; the description adds no syntax or formatting detail for state, years, or county. The phrase 'same address used in property_lookup' is contextual rather than a parameter mapping, which could create mild ambiguity despite the clear 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 identifies a clear resource ('FEMA disaster summary') and an aggregation behavior ('buckets declarations by incident type and year'), which distinguishes it from a raw declaration list. It also ties the tool to a property_lookup workflow, though it does not explicitly rule out the closely related disaster_declarations sibling.
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 a clear use case ('insurance brokers and realtors can assess cumulative risk') and connects the tool to property_lookup. However, it never explicitly states when to use this tool over alternatives like disaster_declarations or disaster_recovery_profile, so selection guidance is largely implied.
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.