Search TapWaterMap for US cities by name. Returns matching cities with an id you can pass to fetch. (ChatGPT/Deep-Research compatible.)
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | City name or partial name, e.g. 'Burlington'. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnly, idempotent, and non-destructive. The description adds the scope (US cities) and clarifies the output ids are for fetch, which is useful context beyond annotations.
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 extremely concise: one sentence plus a parenthetical note. Front-loaded with the main verb and resource, no wasted 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?
For a simple search tool with no output schema, the description adequately explains the return format (matching cities with an id for fetch). Missing details like result limits or error handling, but acceptable for the tool's simplicity.
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 'query' parameter. The description reinforces it accepts city names or partial names but does not add new semantics beyond the schema.
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 it searches for US cities by name and returns ids for fetch. However, it does not differentiate from sibling tools like 'search_cities' or 'find_cities', which may cause confusion.
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 searching cities but provides no explicit guidance on when to use this vs alternatives, nor exclusions. The 'ChatGPT/Deep-Research compatible' note hints at compatibility but not clear context.
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.