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lzinga

US Government Open Data MCP

by lzinga

usda_crop_data

Read-onlyIdempotent

Get U.S. crop production data for corn, soybeans, wheat, cotton, rice, sorghum. Specify year, state, and category to retrieve planted area, harvested area, production, or yield.

Instructions

Get crop production data — area planted, harvested, production, yield. Commodities: CORN, SOYBEANS, WHEAT, COTTON, RICE, SORGHUM

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
yearNoYear (omit for all recent years)
stateNoState code: IA, IL, CA, TX. Omit for national
categoryNoPRODUCTION (default), AREA PLANTED, AREA HARVESTED, YIELD
commodityYesCrop name: CORN, SOYBEANS, WHEAT, COTTON, RICE

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv2026.4.11
    • removedInput schema / additionalProperties
      Removed value: -false
  2. First observedv2026.3.9

TDQS

A3.8/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds domain details (commodities, metrics) but no additional behavioral context like pagination, rate limits, or response format. It does not contradict annotations.

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?

Two compact lines with the core purpose front-loaded, followed by a concise list of metrics and commodities. Every word earns its place, no redundancy or filler.

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 straightforward read-only tool with 100% schema coverage and safety annotations, the description, when combined with the schema, gives an agent enough to invoke it correctly. Minor gaps like return value structure are mitigated by the simple nature of the data and the absence of an 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?

Schema coverage is 100%, so the schema documents all four parameters. The description reiterates category values and commodity names, adding the valid commodity SORGHUM not listed in the schema's commodity description builders. However, it provides no deeper semantic context beyond what the schema already offers.

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 states a specific verb ('Get'), a clear resource ('crop production data'), and enumerates the metrics (area planted, harvested, production, yield). It clearly distinguishes itself from USDA siblings like usda_livestock and usda_prices by focusing on crop production.

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

Usage Guidelines3/5

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

The description implies its use case through content (crop data) but provides no explicit guidance on when to prefer this tool over alternatives such as usda_livestock or usda_prices. No exclusions or comparison to siblings are mentioned.

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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