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cyntrica

Gov Data MCP

by cyntrica

usda_crop_data

Read-only

Fetch crop production statistics including planted area, harvested area, yield, and production for major commodities like corn, soybeans, and wheat, filterable by state and year.

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
Behavior3/5

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

The readOnlyHint annotation already establishes the read-only nature. The description adds the data categories and commodity list, which is useful, but does not disclose response format, pagination, or any default behaviors beyond what the schema states. No contradiction with 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?

The description is two sentences long and immediately leads with the verb and resource. The commodity list is terse and informative, with no extraneous words.

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?

Given the tool has four parameters and no output schema, the description adequately conveys the available crop production categories and commodity scope. It relies on the schema for year/state/category details, which is acceptable. It could be enhanced by noting the default category, but overall it is sufficiently complete for a simple query tool.

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

Parameters4/5

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

Schema description coverage is 100%, so the baseline is 3. The description adds value by listing SORGHUM as a valid commodity, which is missing from the schema's commodity description, and by reiterating the category options. This extra enumeration helps disambiguate valid values.

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

Purpose4/5

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

The description clearly states 'Get crop production data' and enumerates specific metrics (area planted, harvested, production, yield) and commodities, making the tool's purpose unambiguous. It distinguishes from USDA siblings like usda_prices and usda_livestock by focusing on production data, though it does not explicitly reference those alternatives.

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

Usage Guidelines2/5

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

There is no guidance on when to use this tool versus USDA alternatives such as usda_prices, usda_livestock, or usda_ag_query. The description only says what it does, not when it should be preferred over other crop-related tools.

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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