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Nass Crop Production

nass_crop_production
Read-onlyIdempotent

Get US crop yields, production totals, and planted/harvested acreage by crop, state, and year. Quick access to major crop survey data.

Input Schema

NameRequiredDescriptionDefault
yearNoYear or range, e.g., "2024" or "2020:2025" (optional)
stateNoState name, e.g., "IOWA" (optional, defaults to national)
_apiKeyYesNASS API key
commodityYesCrop name: "CORN", "SOYBEANS", "WHEAT", "COTTON", "RICE", "SORGHUM", "BARLEY", "OATS"
stat_categoryNoStatistic: "PRODUCTION", "YIELD", "AREA PLANTED", "AREA HARVESTED" (default: "PRODUCTION")

Output Schema

NameRequiredDescriptionDefault
dataYesCrop production and yield data
countYesNumber of records returned
truncatedYesTrue if results exceed 200 records

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedInput schema / examples
      Previous value: -[
      -  {
      -    "_apiKey": "your-nass-api-key",
      -    "commodity": "CORN",
      -    "stat_category": "PRODUCTION",
      -    "state": "IOWA"
      -  },
      -  {
      -    "_apiKey": "your-nass-api-key",
      -    "commodity": "WHEAT",
      -    "stat_category": "YIELD",
      -    "year": "2020:2025"
      -  }
      -]New value: +[
      +  {
      +    "commodity": "CORN",
      +    "stat_category": "PRODUCTION",
      +    "state": "IOWA"
      +  },
      +  {
      +    "commodity": "WHEAT",
      +    "stat_category": "YIELD",
      +    "year": "2020:2025"
      +  }
      +]
  2. Changed1 schema field changed
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
      +  "properties": {
      +    "count": {
      +      "description": "Number of records returned",
      +      "type": "integer"
      +    },
      +    "data": {
      +      "description": "Crop production and yield data",
      +      "items": {
      +        "properties": {
      +          "agg_level": {
      +            "description": "Aggregation level",
      +            "type": [
      +              "string",
      +              "null"
      +            ]
      +          },
      +          "commodity": {
      +            "description": "Crop name",
      +            "type": [
      +              "string",
      +              "null"
      +            ]
      +          },
      +          "county": {
      +            "description": "County name",
      +            "type": [
      +              "string",
      +              "null"
      +            ]
      +          },
      +          "description": {
      +            "description": "Statistic description",
      +            "type": [
      +              "string",
      +              "null"
      +            ]
      +          },
      +          "frequency": {
      +            "description": "Frequency",
      +            "type": [
      +              "string",
      +              "null"
      +            ]
      +          },
      +          "period": {
      +            "description": "Reference period",
      +            "type": [
      +              "string",
      +              "null"
      +            ]
      +          },
      +          "stat_category": {
      +            "description": "Statistic (PRODUCTION, YIELD, AREA PLANTED, AREA HARVESTED)",
      +            "type": [
      +              "string",
      +              "null"
      +            ]
      +          },
      +          "state": {
      +            "description": "State name",
      +            "type": [
      +              "string",
      +              "null"
      +            ]
      +          },
      +          "unit": {
      +            "description": "Unit of measurement",
      +            "type": [
      +              "string",
      +              "null"
      +            ]
      +          },
      +          "value": {
      +            "description": "Production, yield, or acreage value",
      +            "type": [
      +              "string",
      +              "null"
      +            ]
      +          },
      +          "year": {
      +            "description": "Year of the record",
      +            "type": [
      +              "integer",
      +              "null"
      +            ]
      +          }
      +        },
      +        "type": "object"
      +      },
      +      "type": "array"
      +    },
      +    "truncated": {
      +      "description": "True if results exceed 200 records",
      +      "type": "boolean"
      +    }
      +  },
      +  "required": [
      +    "count",
      +    "data",
      +    "truncated"
      +  ],
      +  "type": "object"
      +}
  3. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
      +  {
      +    "_apiKey": "your-nass-api-key",
      +    "commodity": "CORN",
      +    "stat_category": "PRODUCTION",
      +    "state": "IOWA"
      +  },
      +  {
      +    "_apiKey": "your-nass-api-key",
      +    "commodity": "WHEAT",
      +    "stat_category": "YIELD",
      +    "year": "2020:2025"
      +  }
      +]
  4. First observed

TDQS

A3.6/5.0
Behavior3/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, so the safety profile is well covered. The description adds only the scope of data returned ('major crop survey data') and does not disclose rate limits, data vintage, or API-key requirements beyond the schema. This is acceptable because the annotations carry the behavioral burden.

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 short sentences front-load the actionable content and add one scoping phrase. There is no filler or repetition of schema details.

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 5-parameter read-only query tool with 100% schema coverage and an output schema, the description covers the core purpose and data domain. It does not mention alternatives or limitations, but nothing needed to invoke the tool correctly is missing from the combined definition.

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 description coverage is 100% and includes examples and defaults, so the schema already documents all five parameters. The description recaps the dimensions (crop, state, year, statistic) but adds no format, syntax, or default information beyond the schema. Baseline 3 is appropriate.

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 opens with a specific verb and resource: 'Get US crop yields, production totals, and planted/harvested acreage by crop, state, and year.' This clearly identifies the data domain and distinguishes it in substance from livestock, prices, and progress siblings, though it does not name an alternative tool.

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 when to use it: whenever crop yield, production, or acreage figures are needed. It does not explicitly state exclusions or point to siblings like nass_crop_progress, nass_livestock, or nass_prices as alternatives, so usage guidance is left to inference.

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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