USDA Food Access

get_agricultural_data

Get USDA NASS QuickStats data on agricultural production by state.

Returns crop values, production quantities, and farm statistics from the
USDA National Agricultural Statistics Service. Useful for understanding
local food production capacity in grant narratives.

Args:
    state: Two-letter state abbreviation (e.g. 'WA', 'IA').
    commodity: Agricultural commodity to query (e.g. 'CORN', 'SOYBEANS', 'WHEAT',
               'CATTLE', 'MILK'). Omit to get a broad survey of crops.
    year: Year for data (e.g. 2022). Omit to get the most recent available data.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`year`	No
`state`	Yes
`commodity`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

TDQS

A4.4/5.0

Behavior3/5

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

No annotations are provided, so the description carries the full burden. It discloses the data source (USDA NASS) and what data is returned, but omits details like authentication needs, rate limits, response size, or error behavior. For a read-only data retrieval tool, this is moderate but not insufficient.

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 a two-sentence summary followed by a compact, readable Args list. Every sentence provides useful information, and the most important purpose is front-loaded.

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 a small parameter set (3), an existing output schema, and clear descriptions of return types and use context, the description covers all essential aspects for successful invocation. No critical details are missing.

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 description coverage is 0%, but the 'Args' block compensates with detailed explanations and examples for each parameter, including default behavior when omitted (e.g., 'Omit to get broad survey', 'Omit to get most recent available data'). This adds substantial meaning beyond 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 opens with a specific verb+resource: 'Get USDA NASS QuickStats data on agricultural production by state.' It clearly states the return types (crop values, production quantities, farm statistics) and is easily distinguished from sibling tools focused on food environment, food insecurity, and 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 provides clear use context: 'Useful for understanding local food production capacity in grant narratives.' It does not explicitly name alternatives or exclusions for sibling tools, but the context makes when-to-use apparent, so no misleading guidance.

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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