Search individual US healthcare providers by name. Requires a last_name (first_name, state, city optional). Returns NPI, specialty, location for each match.
| Name | Required | Description | Default |
|---|---|---|---|
| city | No | City to narrow results (optional). | |
| limit | No | Max results (1-50, default 10). | |
| state | No | Two-letter state code to narrow results (optional). | |
| last_name | Yes | Provider last name (required). | |
| first_name | No | Provider first name (optional). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, openWorldHint=true, and destructiveHint=false, covering the safety profile. The description adds useful behavioral context by specifying the returned data fields (NPI, specialty, location), which is beyond what annotations or the schema state. It does not discuss matching behavior, pagination, or rate limits, but for a simple read-only search this is 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 sentences with no wasted words. The first sentence states the core purpose, and the second provides the required/optional input expectation plus the output summary. It is front-loaded and every sentence earns its place.
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 five-parameter, single-required-field read-only search tool, the description covers the necessary input rule and the expected return fields. There is no output schema, so describing the key returned values is helpful and sufficient. It could have explicitly mentioned the limit parameter or behavior when no matches are found, but the schema already covers limit and this is not a blocking 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 the input schema fully documents all five parameters. The description restates that last_name is required and that first_name, state, and city are optional, which adds minimal value beyond the schema. The limit parameter is only present in the schema, but the baseline of 3 is appropriate when the schema already carries param semantics.
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 uses a specific verb ('Search'), identifies the resource ('individual US healthcare providers'), and narrows the search to 'by name.' The word 'individual' clearly distinguishes this from sibling tools like npi_search_organization and npi_search_specialty, so an agent can tell them apart.
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 clearly conveys that last_name is required and that first_name, state, and city are optional narrowing filters. It does not explicitly name alternatives or state 'use X instead for organizations,' but the 'individual' qualifier plus sibling tool names provide clear context for when this tool is appropriate.
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.