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xippios-mcp

xippios-mcp

by xippios
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

usda_nass_get_acreage

Read-onlyIdempotent

Retrieve final planted or harvested acreage for a crop by year and two-letter state code, or the US national total, excluding size, irrigation and in-season forecast breakdowns.

Instructions

Planted or harvested acreage of a crop for a year, state or national, in acres.

Annual final totals (unit_desc ACRES, domain_desc TOTAL, prodn_practice_desc ALL PRODUCTION PRACTICES, reference_period_desc YEAR), so breakdowns by size group, irrigation or biotech type and the in-season forecasts are left out; get_acreage_forecasts serves the latter. Harvested acreage comes per utilisation (grain, silage); planted acreage as one figure. Sorted by state, utilisation and source.

Args: commodity: Upstream commodity_desc, e.g. "CORN", "SOYBEANS", "WHEAT". year: Crop year, e.g. 2023. statistic: AreaStatistic.AREA_PLANTED or AreaStatistic.AREA_HARVESTED. state: Two-letter state code for the state figure; None returns the national US TOTAL figure.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "commodity": "<commodity>",
      "year": "<year>",
      "statistic": "<statistic>"
    },
    "name": "usda_nass_get_acreage"
  }
}

Input Schema

NameRequiredDescriptionDefault
yearYes
stateNo
commodityYes
statisticYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.7/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, openWorld and non-destructive, so the safety profile is covered. The description adds real substance beyond that: the exact upstream filters (unit_desc ACRES, domain_desc TOTAL, prodn_practice_desc ALL PRODUCTION PRACTICES, reference_period_desc YEAR) that define 'annual final totals', plus the stated sort order by state, utilisation and source. It stops short of describing row counts, pagination, or release lag, which keeps it from a 5.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with purpose in sentence one, then scope exclusions, then per-argument notes — a sensible ordering. It is dense with quoted upstream field names, which is justified by the precision it buys, but the parenthetical filter list is heavier than strictly necessary for a reader who only needs to pick the tool.

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?

For a four-parameter, no-output-schema query tool, the description supplies everything needed to call it correctly: the population it returns, the aggregation level, the planted/harvested distinction, the state-vs-national switch, and the result ordering. Nothing material about invocation is left to inference.

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% at the property level, but the Args block documents all four parameters: commodity as upstream commodity_desc with concrete examples (CORN, SOYBEANS, WHEAT), year as crop year, statistic as the two AreaStatistic values, and state as a two-letter code where None returns the national US TOTAL figure. That fully compensates for the schema gap and adds semantics the schema does not carry.

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?

Opens with a specific verb-and-resource statement: planted or harvested acreage of a crop for a year, at state or national level, in acres. It also distinguishes itself from the sibling usda_nass_get_acreage_forecasts in the same paragraph, so an agent can route without opening either schema.

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

Usage Guidelines5/5

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

Explicitly states what the tool does not cover — breakdowns by size group, irrigation or biotech type, and in-season forecasts — and names get_acreage_forecasts as the alternative for the excluded forecasts. It also clarifies the planted-vs-harvested split (harvested per utilisation, planted as one figure), which is the main selection ambiguity.

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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