crop-prices

get_current_price

Retrieve the current price for a single US crop, trying live futures first, then USDA farm prices, then global monthly benchmarks. Check fallback_used and age_days to see source freshness.

Instructions

Get the most recent price for one US crop. Use for "what is X selling for now?"

Tries live futures first, then USDA farm prices, then the global monthly price. Check fallback_used and age_days: farm and global prices can be 1-2 months old, and nuts/tobacco/sugarbeets only have annual prices.

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>"
    },
    "name": "get_current_price"
  }
}

Input Schema

Name	Required	Description	Default
`commodity`	Yes	Crop name or alias, e.g. "soybeans", "KC wheat", "alfalfa".

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Nov 10, 2026

TDQS

A4/5.0

Behavior4/5

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

With no annotations, the description carries the full burden, and it discloses the real behavioral trait: a three-tier fallback chain (live futures, then USDA farm prices, then global monthly). It also names the tell-tale response fields (`fallback_used`, `age_days`) and warns that some commodities only have annual data. It does not state what happens when every source fails or whether the price is adjusted for units.

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?

Three short lines, front-loaded with the purpose, then the use case, then the fallback/staleness caveat. Every sentence carries information an agent would otherwise have to discover by trial.

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?

There is no output schema, so the description must telegraph the return shape, and it does by naming `fallback_used` and `age_days` and explaining why they matter. What is missing is the identity of the price field itself, its units, and error behavior when no source has data.

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 the schema already gives the parameter name, alias support, and examples, so the baseline applies. The description adds only the 'one US crop' restriction, which is a modest scope clarification rather than format or syntax detail.

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?

States a specific verb and resource ('get the most recent price') plus a scope constraint ('for one US crop'), which tells an agent this is a single-commodity spot lookup rather than a history or comparison tool. It stops short of naming the sibling that covers history, so the distinction is implied by 'most recent' rather than stated.

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?

Gives an explicit use case in the user's own phrasing ('what is X selling for now?'), which is exactly the trigger condition an agent needs. It offers no explicit when-not guidance or named alternative for historical/comparative queries, so the routing is clear but not complete.

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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