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LiveDataLink

by io.github.blackboxfoundry

disaster_recovery_profile

Read-onlyIdempotent

One-call disaster-history and recovery read for a US area (county or place), keyed by NAME + state - distinct from location_risk_report, which scores a single site by address/lat-lon. Joins FEMA's OpenFEMA disaster declarations (the area's federally-declared disaster history: incident types, frequency, most-recent event, and the federal-assistance signal - which programs, Individual Assistance / Individuals & Households / Public Assistance / Hazard Mitigation, were authorized) with optional US Census ACS county population for exposure context (keyed off the FIPS codes the FEMA records carry; needs a Census key and degrades gracefully) and an optional best-effort parcel record for property context when an address is given (Maryland statewide / Texas-Harris County only). Returns a readable profile with a headline banding the area's disaster exposure LOW / MODERATE / HIGH from the declaration record. The FEMA leg is keyless and is the core signal; a source that fails is noted, not fatal. INFORMATIONAL public-record synthesis, NOT an insurance rating, a property flood-risk score, or a professional risk assessment.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
areaYesCounty or place name to profile (e.g. 'Harris', 'Orleans', 'New Orleans'). Use the name without the word 'County'.
stateNo2-letter US state code (or full state name) to scope the FEMA history (e.g. 'TX', 'LA'). Strongly recommended - required for an accurate county read.
countyNoAlias for area; either may be supplied.
addressNoOptional street address to attach a sample parcel/property record (Maryland statewide or Texas-Harris County only).

TDQS

A4.9/5.0
Behavior5/5

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

The annotations already mark the tool read-only and idempotent, and the description adds substantial behavioral context beyond that: FEMA data is keyless, failing sources are noted rather than fatal, Census integration degrades gracefully, and the output is explicitly informational rather than an insurance or flood-risk rating. This gives the agent accurate expectations about reliability and scope.

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 dense but every clause earns its place: purpose, sibling differentiation, data sources, output format, failure behavior, and disclaimers. It is front-loaded with the core purpose and avoids filler, making it appropriately sized for a multi-source synthesis tool.

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

Completeness5/5

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

For a complex tool with no output schema, the description is remarkably complete: it explains the output profile and its headline risk band, lists all joined data sources, notes geographic limitations for optional inputs, and states failure behavior. An agent has enough context to invoke it correctly and interpret the result.

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?

The schema already covers all four parameters at 100%, so the baseline is 3. The description adds value by explaining the role of optional inputs: Census population requires a key and degrades gracefully, and address only works for parcel context in specific geographies. This goes beyond the schema's descriptions, though not exhaustively.

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

Purpose5/5

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

The description opens with a specific verb and resource: 'One-call disaster-history and recovery read for a US area (county or place), keyed by NAME + state.' It also explicitly contrasts itself with location_risk_report, making the tool's scope and differentiator unmistakable.

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

Usage Guidelines5/5

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

It states when to use this tool (area-level disaster history by name/state) and names the alternative for site-level risk scoring (location_risk_report by address/lat-lon). It also clarifies when optional inputs apply (Census key, parcel address, Maryland/Texas-Harris coverage), giving an agent actionable selection criteria.

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.

Resources