crop-prices

compare_commodities

Compare crops by percent price change over a chosen period to normalize differing price units and identify relative market movement using futures or fallback farm/global price history.

Instructions

Compare crops by % price change over the same period. Units differ, so raw prices are not comparable, but % change is. Uses each crop's futures history when available, otherwise its monthly farm or global price.

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": {
      "commodities": "<commodities>"
    },
    "name": "compare_commodities"
  }
}

Input Schema

Name	Required	Description	Default
`months`	No	Compare % change over this many months.
`commodities`	Yes	Crop names, e.g. ["corn", "soybeans", "wheat"].

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Nov 10, 2026

TDQS

A3.6/5.0

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 does disclose a genuinely important trait: the data source varies per crop (futures history vs. monthly farm or global price), warning that comparisons may mix sources. It does not cover error behavior for unknown crop names or any ordering/format of results, so it is informative but not exhaustive.

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?

Three short sentences, front-loaded with the action and metric, and the justification sentence earns its place by explaining why percentages are used. Minor line-break artifacts aside, there is no redundant wording.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple two-parameter read tool this is largely sufficient, but with no output schema the description never states the shape of the result (per-crop values, ordering, how crops with differing source data are reported). An agent knows what it will get conceptually but not how to interpret the response.

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% with both parameters fully described, so baseline is 3. The description reinforces the 'same period' framing behind the months parameter but adds no new syntax, defaults, or constraints beyond what the schema already states.

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?

Names a specific verb and resource ('Compare crops') and pins the metric to '% price change over the same period', which clearly separates it from price-listing siblings like get_price_history. It stops short of naming an alternative tool explicitly, so it is clear but not fully differentiated.

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 rationale sentence ('Units differ, so raw prices are not comparable, but % change is') implicitly tells the agent to use this instead of raw-price tools when cross-crop comparison is needed. However, there is no explicit when-to-use/when-not-to-use statement or named alternative, leaving the routing inference to the agent.

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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