Search Medicare-certified nursing homes from CMS Nursing Home Compare. Returns name, address, ownership, certification status, total beds, and quality measures.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max results (default 25) | |
| state | No | Two-letter state code (e.g. 'CA', 'NY') | |
| offset | No | Pagination offset (default 0) | |
| name_contains | No | Partial provider name match |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already declare the tool read-only, idempotent, non-destructive, and open-world. The description adds useful behavioral context by specifying the Medicare-certified scoping constraint and the concrete fields returned, which is beyond the annotations. It does not mention rate limits or data freshness, but no such concerns are strongly implied.
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 a single, information-dense sentence with no filler. It front-loads the action and resource, then lists the returned fields, making every phrase useful.
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 simple, fully optional-parameter read-only search, the description covers the core purpose, source, and output fields. The schema covers filtering and pagination details. It could be slightly more complete by hinting at the result structure or search behavior, but nothing essential for invoking the tool correctly is missing.
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%, and each parameter already has a clear explanation ('Two-letter state code', 'Partial provider name match', etc.). The description adds no additional parameter-level meaning, so the baseline score of 3 is appropriate.
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 names the action ('Search'), the resource ('Medicare-certified nursing homes'), and the data source ('CMS Nursing Home Compare'). It also enumerates the returned fields, and the nursing-home scope distinguishes it from sibling tools like cms_hospital_search, cms_home_health_search, and cms_hospice_search.
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 tool's intended use is implied strongly by the provider type in the description, but there is no explicit 'use this when' or 'use sibling X instead' guidance. An agent can infer when to use it, but the description leaves that inference entirely to the reader.
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.