search_healthcare_providersSearch CMS hospitals and nursing homes by state with quality ratings. Returns overall star ratings, safety/mortality comparisons for hospitals, and inspection/staffing ratings for nursing homes.
| Name | Required | Description | Default |
|---|---|---|---|
| city | No | City name to filter results | |
| type | No | Provider type (default: both) | |
| state | Yes | Two-letter state code (e.g., CA) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint true and openWorldHint true, so the tool is safe and potentially large result sets. The description adds context by detailing the returned information (star ratings, safety comparisons for hospitals, staffing ratings for nursing homes), complementing the annotations well.
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, front-loaded with the action and scope. Every sentence adds value: the first defines the tool, the second specifies return data. No unnecessary words.
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?
Given the tool has no output schema but the description lists the key return fields, and annotations cover safety and open-world hints, the description is complete enough for an agent to understand what to expect. Parameters are fully documented in the schema.
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?
All three parameters are fully described in the schema (100% coverage). The description mentions 'by state' and implies provider types (hospital vs nursing home), but adds no new syntactic or semantic details beyond what the schema provides. Baseline 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 states 'Search CMS hospitals and nursing homes by state with quality ratings', specifying both the verb and resource. It distinguishes itself from sibling tools like 'search_colleges' by focusing on healthcare providers and listing specific return details.
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 implies usage for querying healthcare providers by state, and no sibling tool performs similar searches, so context is clear. However, it does not explicitly state when not to use or provide alternatives.
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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Each tool has a clear, specific purpose with detailed descriptions that differentiate them. Prefix patterns like get_district_, search_, analyze_, get_, etc., help an agent easily identify the correct tool for a task.
All tool names use a consistent verb_noun or verb_noun_noun pattern with underscores. The naming convention is uniform across the entire set, with no mixing of styles or ambiguous verbs.
With 47 tools, the count is high but justified by the broad scope of civic data analysis. While some agents might find the sheer number overwhelming, the tools are organized into clear categories (district profiles, searches, analyses) that make navigation feasible.
The toolset covers an impressively wide range of domains: legislation, representatives, districts, voting, committees, campaign finance, lobbying, federal spending, regulations, environment, energy, healthcare, housing, disaster, banking, consumer complaints, crime, vehicles, and more. There are no obvious missing operations for a civic data platform.