Fetch the full plain-English EPA tap-water record for a city id (e.g. 'vt/burlington') returned by search. (ChatGPT/Deep-Research compatible.)
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | City id like 'vt/burlington' (state/slug). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint. The description adds that it returns a 'full plain-English... record', giving insight into the output format. This is useful beyond annotations, though no mention of other behaviors like rate limits or prerequisites.
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?
A single sentence with a parenthetical example and a compatibility note. Every word is purposeful, no redundancy.
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 one simple parameter, complete annotations, and no output schema, the description hints at the return format ('full plain-English... record') adequately. Users may need assurance about pagination or size, but for a single-record fetch it is sufficient.
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 coverage is 100% with a clear description of the 'id' parameter. The description adds the crucial context that the id comes from 'search', which helps the agent understand the source of valid values.
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 the action 'Fetch' and the resource 'full plain-English EPA tap-water record' for a specific city id. It explicitly references the sibling tool 'search' as the source of the id, distinguishing this tool as the follow-up retrieval step.
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 provides explicit context: use the id returned by 'search'. It also notes ChatGPT/Deep-Research compatibility. However, it does not explicitly state when not to use this tool or list alternatives, though the sibling list implies differentiation.
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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Some tools have overlapping purposes: search and search_cities both find cities by name, and fetch and get_city_water both retrieve detailed records for a city. However, descriptions differentiate them (e.g., search returns an id, search_cities returns counts and URL), so an agent can often pick correctly, but confusion is possible.
Tool names mix conventions: single-word verbs (fetch, search), verb_noun (compare_cities, find_cities), and noun_noun (coverage_stats). There is no consistent pattern, and similar tools have different naming styles (e.g., search vs search_cities).
10 tools is well-suited for querying EPA tap water data. Each tool serves a distinct purpose within the domain, and the count is neither too few (limiting functionality) nor too many (overwhelming).
The tool surface covers key operations: searching cities, retrieving records, comparing, filtering, explaining contaminants, and coverage stats. Minor gaps exist (e.g., no way to search contaminants by partial name), but core workflows are supported.