EPA Air Quality System (AQS) MCP Server

aqs_sample_data_by_state

Retrieve air quality sample measurements from all monitoring sites in a specified state for analysis, with warnings about large data volumes requiring limited date ranges.

Instructions

Get raw sample data for all monitoring sites in a state. WARNING: Sample data can be EXTREMELY large for state-level queries. Strongly recommend limiting date ranges to one week or less. Returns individual sample measurements from all sites in the specified state.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "param": "<param>",
      "bdate": "<bdate>",
      "edate": "<edate>",
      "state": "<state>"
    },
    "name": "aqs_sample_data_by_state"
  }
}

Input Schema

Name	Required	Description
`email`	No	Email address for API authentication. Optional if AQS_EMAIL environment variable is set.
`key`	No	API key for authentication. Optional if AQS_API_KEY environment variable is set.
`param`	Yes	Parameter code (e.g., 44201 for Ozone, 88101 for PM2.5, 42401 for SO2, 42101 for CO, 42602 for NO2, 81102 for PM10).
`bdate`	Yes	Begin date in YYYYMMDD format. Must be in the same calendar year as edate.
`edate`	Yes	End date in YYYYMMDD format. Must be in the same calendar year as bdate.
`state`	Yes	Two-digit FIPS state code (e.g., "06" for California, "36" for New York).

Schema Changelog

Changes observed during successful MCP inspections.

First observedJan 28, 2026

TDQS

A4.4/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively warns about performance implications ('EXTREMELY large'), provides practical usage advice ('limit date ranges'), and clarifies the return format ('individual sample measurements'). However, it doesn't mention authentication requirements (covered in schema) or potential rate limits, leaving some gaps.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is efficiently structured with three sentences: purpose statement, critical warning with recommendation, and return value clarification. Every sentence adds essential information without redundancy, and the warning is appropriately front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a data retrieval tool with no annotations and no output schema, the description provides strong context about data volume and usage constraints. It covers the core purpose and behavioral considerations well, though it doesn't describe the output structure (only mentions 'individual sample measurements'), which would be helpful given the lack of output schema.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema description coverage is 100%, so the baseline is 3. The description adds minimal value beyond the schema by implying that parameters define the query scope (state, date range, pollutant), but doesn't provide additional semantic context like examples or edge cases beyond what's already documented in the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose with specific verbs ('Get raw sample data') and resources ('for all monitoring sites in a state'), distinguishing it from sibling tools like aqs_sample_data_by_county or aqs_sample_data_by_site by specifying the geographic scope. It also explains what the data represents ('individual sample measurements from all sites').

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit guidance on when to use this tool with a strong warning about data size ('EXTREMELY large for state-level queries') and a specific recommendation to limit date ranges ('one week or less'). It implicitly distinguishes from summary tools (e.g., aqs_annual_summary_by_state) by emphasizing raw sample data, though it doesn't name alternatives directly.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.