Detailed air quality data for one ZIP code. Use when get_zip_profile's air quality section isn't sufficient depth. Includes EPA AQS pollutant levels (PM2.5, PM10, ozone, NO2, SO2, CO) and AQI summary.
| Name | Required | Description | Default |
|---|---|---|---|
| zip | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description lists the included data types (EPA AQS pollutant levels and AQI summary), which gives a good idea of the output. However, with no annotations provided, it omits important behavioral details such as data freshness, rate limits, or authentication requirements. The description is adequate but not thorough.
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: two sentences that front-load the purpose followed by usage guidance. Every word earns its place without unnecessary elaboration.
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 data retrieval tool with one parameter and no output schema, the description covers purpose, usage context, and high-level contents. It could mention output format or any prerequisites, but overall it provides sufficient context for an AI agent.
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?
The only parameter 'zip' is a ZIP code, which is self-explanatory. Despite 0% schema description coverage, the parameter is obvious and the description does not need to add further meaning. The baseline of 4 is appropriate given the parameter's clarity.
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 tool's purpose: providing detailed air quality data for a single ZIP code. It uses a specific verb ('get') and resource ('air quality profile'), and distinguishes from the sibling 'get_zip_profile' tool by noting that this tool offers deeper air quality information.
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 explicitly states when to use this tool: when 'get_zip_profile' air quality section is insufficient. This provides clear context for selection, though it does not mention scenarios where the tool should not be used or suggest other alternatives.
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.
Every tool has a clearly distinct purpose. Domain-specific profiles (e.g., get_air_quality_profile) are explicitly for deeper dives after get_zip_profile, while search, reverse geocode, research, and methodology tools each serve unique roles with no overlap.
All tools follow a consistent verb_noun pattern: get_<domain>_profile, search_zips, reverse_geocode, compare_zips, get_research, get_methodology, etc. The naming is predictable and clear, with no mixing of conventions.
22 tools is slightly above the typical well-scoped range, but each tool addresses a specific need (12 domain profiles, search, geocoding, articles, methodology, ping). The count is appropriate for the breadth of data offered.
The tool surface covers the full workflow: discover (search_zips, get_search_fields), retrieve (get_zip_profile, domain profiles), compare (compare_zips), and ancillary (reverse_geocode, get_research, get_methodology). There are no dead ends or obvious gaps.
