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Usda Nass

agriculture__usda-nass
Read-onlyIdempotent

Query USDA NASS agricultural statistics for crops and livestock by commodity, year, and state to access production, yield, and price data with quality scoring and source verification.

Instructions

[Agriculture & Food Agent] Query the USDA National Agricultural Statistics Service (NASS) Quick Stats API for crop and livestock statistics by commodity, year, and state. Source: USDA NASS (Public Domain (U.S. Government)), updates monthly. Returns the Katzilla envelope { data, quality, citation } — quality scores freshness/uptime/confidence; citation carries the source URL, license, and a SHA-256 data hash for audit.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
commodityNoCommodity description (e.g. CORN, SOYBEANS, WHEAT)
statisticCategoryNoStatistic category (e.g. PRODUCTION, AREA HARVESTED, YIELD, PRICE RECEIVED)
stateNoState alpha code (e.g. IA, IL, TX). Omit for national totals.
commodity_descNoCommodity description using USDA field name (e.g. CORN, SOYBEANS, WHEAT)
statisticcat_descNoStatistic category using USDA field name (e.g. PRODUCTION, AREA HARVESTED)
state_alphaNoState alpha code using USDA field name (e.g. IA, IL, TX)
yearNoStatistics year (USDA NASS data lags ~1 year)
aggLevelNoAggregation level: NATIONAL, STATE, COUNTY, or REGIONNATIONAL
agg_level_descNoAggregation level using USDA field name
limitNoMaximum rows to return

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataYesStructured payload from the upstream source.
textNoPre-rendered text representation, when applicable.
qualityYesQuality scorecard: freshness, uptime, completeness, confidence, certainty.
citationYesProvenance block — source, license, retrieval timestamp, SHA-256 data hash, pre-formatted citation text.
Behavior4/5

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

The description adds valuable behavioral context beyond the annotations. Annotations indicate read-only, non-destructive, idempotent, and open-world traits. The description supplements this by specifying the data source (USDA NASS, Public Domain), update frequency (monthly), and the return format (Katzilla envelope with data, quality scores, and citation details including a SHA-256 hash for audit). This enriches the agent's understanding of data freshness, licensing, and output structure, though it doesn't cover rate limits or error handling.

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 highly concise and front-loaded. The first sentence clearly states the tool's purpose and key parameters. The second sentence efficiently adds source, update frequency, and output format details. Every sentence earns its place with no wasted words, making it easy for an agent to parse quickly.

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 tool's complexity (10 parameters, annotations, and an output schema), the description is complete enough. It covers purpose, source, update frequency, and output structure. With annotations providing safety and idempotency hints, and an output schema presumably detailing the Katzilla envelope, the description fills in necessary contextual gaps without redundancy. It adequately prepares an agent for effective use.

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 input schema has 100% description coverage, so all parameters are well-documented in the schema itself. The description does not add any parameter-specific semantics beyond what the schema provides (e.g., it mentions 'by commodity, year, and state' but the schema already details these). With high schema coverage, the baseline is 3, as the description doesn't compensate with extra param info but doesn't need to.

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: 'Query the USDA National Agricultural Statistics Service (NASS) Quick Stats API for crop and livestock statistics by commodity, year, and state.' It specifies the exact resource (USDA NASS Quick Stats API), the action (query), and the data domain (crop and livestock statistics). This distinguishes it from sibling tools like agriculture__usda-ers or agriculture__usda-fooddata, which target different USDA data sources.

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 context for when to use this tool: for querying USDA NASS agricultural statistics. It implicitly suggests alternatives by mentioning the source (USDA NASS) and data type (crop and livestock statistics), but does not explicitly state when not to use it or name specific alternative tools for similar queries. The context is sufficient for an agent to infer usage, but lacks explicit exclusions or direct sibling comparisons.

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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