USDA Food Access

get_food_environment

Get county-level food access risk profiles using Census ACS data.

Constructs food access risk profiles by combining vehicle access (B25044),
poverty status (B17001), and SNAP participation (B22001). Limited vehicle
access combined with high poverty indicates food desert risk. Useful for
identifying areas with barriers to food access in grant applications.

Args:
    state: Two-letter state abbreviation (e.g. 'WA', 'MS') or 2-digit FIPS code.
    county_fips: Three-digit county FIPS code (e.g. '033' for King County, WA).
                 Omit to get all counties in the state.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`state`	Yes
`county_fips`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

TDQS

A4.6/5.0

Behavior4/5

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

With no annotations, the description carries the full burden and explains the tool's behavior: it 'constructs food access risk profiles' by combining specific ACS variables and outlines the risk logic (limited vehicle access + high poverty). It doesn't disclose rate limits or error behavior, but for a read-only data tool this is reasonably transparent.

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 compact and front-loaded with the purpose, followed by methodology, use case, and parameter details. Every section earns its place, and the Args section is efficiently structured for agent consumption.

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

Completeness5/5

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

Given the output schema is present, the description doesn't need to detail return values. It covers the tool's purpose, method, parameters, and use case, making it complete for a two-parameter data-access tool.

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

Parameters5/5

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

Schema coverage is 0%, but the comment block fully compensates: it specifies accepted formats for state ('two-letter abbreviation or 2-digit FIPS'), the three-digit format and example for county_fips, and defines the omit behavior for county_fips. This adds substantial meaning beyond the bare 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 opening line states a specific verb and resource: 'Get county-level food access risk profiles using Census ACS data.' It further distinguishes the tool by describing its unique method (combining vehicle access, poverty, and SNAP) and food desert risk, separating it from sibling tools like get_snap_participation.

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

Usage Guidelines4/5

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

The description gives clear usage context: 'Useful for identifying areas with barriers to food access in grant applications.' It does not explicitly name alternative tools or when-not-to-use scenarios, but the context is sufficient to infer appropriate use.

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

All USDA Food Access tools Inspect

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TDQS

A4.3/5.0

Disambiguation2/5

Three of the four tools (get_food_environment, get_food_insecurity_indicators, get_snap_participation) are highly overlapping, all operating at the county level with ACS data and combining SNAP, poverty, and income indicators. An agent would struggle to distinguish between them, as their purposes and outputs are nearly interchangeable. Only get_agricultural_data is clearly distinct.

Naming Consistency5/5

All tools follow a consistent 'get_<descriptive_noun>' pattern with snake_case. The verbs are uniform and the nouns clearly indicate the data being retrieved. This is a highly predictable naming scheme.

Tool Count5/5

Four tools is a well-scoped count for a specialized data access server. Each tool addresses a distinct aspect of food access data (agricultural production, food environment, food insecurity, SNAP participation), and the count is neither sparse nor overwhelming.

Completeness4/5

The server covers the core data needs for understanding food access: agricultural production, food environment risk, food insecurity indicators, and SNAP participation. Minor gaps exist, such as lack of food price or food store location data, but these are not fundamental to the server's stated purpose and agents can work around them.

Resources

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