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LiveDataLink

by io.github.blackboxfoundry

npi_search_specialty

Read-onlyIdempotent

Find healthcare providers by specialty (taxonomy description) in a location. Requires taxonomy (e.g. 'Cardiology', 'Pediatrics', 'Nurse Practitioner'); state and city optional but recommended.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cityNoCity to narrow results (optional).
limitNoMax results (1-50, default 10).
stateNoTwo-letter state code to narrow results (optional).
taxonomyYesSpecialty / taxonomy description, e.g. 'Cardiology'.

TDQS

A3.7/5.0
Behavior3/5

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

Annotations already establish the read-only, idempotent, non-destructive profile, so the description's burden is lower. It adds useful scoping context ('in a location' and state/city recommendation) but does not disclose result shape, pagination/limit behavior, or any search quirks.

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?

Two compact sentences front-load the core purpose and required input before mentioning optional parameters. Every clause carries useful guidance with no repetition of schema details.

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

Completeness4/5

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

For a simple search tool with strong annotations and 100% schema coverage, the description is nearly sufficient: it covers the required parameter, optional narrowing, and recommended usage. It omits only explicit sibling routing and result-format expectations, which are not critical for invocation.

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 input schema covers all four parameters at 100%, so the baseline is 3. The description adds value beyond the schema with multiple taxonomy examples ('Cardiology', 'Pediatrics', 'Nurse Practitioner') and the explicit recommendation that state and city be supplied, which helps agents form better queries.

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

Purpose4/5

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 ('Find healthcare providers by specialty') and clarifies the search dimension (taxonomy description), which distinguishes it from NPI lookup and organization-search siblings. It does not explicitly name sibling tools, so differentiation is implicit rather than explicit.

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

Usage Guidelines3/5

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

The description clearly states that taxonomy is required and positions state/city as optional but recommended, giving concrete invocation context. However, it does not state when to prefer npi_lookup, npi_search_provider, or npi_search_organization instead, leaving usage guidance to inference.

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.

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