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sell_hold_decision

Read-only

Analyze commodity price trends and storage costs to recommend whether to sell your harvest now or hold for later.

Instructions

Give a sell/hold recommendation for a commodity based on price trends. Western parallel: CME basis trading decision tools, grain elevator advisory. This is the 'what should I do with my harvest today?' AI advisor. DEMO model — always verify prices before acting.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
commodityYesYour commodity
marketYesYour nearest market
quantity_bagsYesQuantity you want to sell (bags or units)
storage_cost_per_month_kesNoYour storage cost per month in KES (0 if storing is free)
months_can_storeNoMaximum months you can store the commodity

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

Annotations already declare readOnlyHint: true. The description adds important behavioral context: it's a DEMO model and recommends verifying prices, indicating reliability limitations beyond the annotation.

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 three sentences, front-loads the purpose, and every sentence adds value (purpose, parallel, caveat). No wasted 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?

With an output schema present, the description adequately explains the tool's purpose and context ('harvest advisor'). It lacks explicit mention of the output type (recommendation) but the demo warning adds necessary caution. Sufficient for a read-only advisory tool.

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?

Input schema has 100% description coverage for all parameters. The description does not add significant detail beyond the schema; it provides general context (e.g., storage cost in KES) but no syntax or format specifics. Baseline 3 is appropriate.

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?

The description clearly states 'Give a sell/hold recommendation for a commodity based on price trends', specifying the verb and resource. It also differentiates from sibling tools (e.g., commodity_price_query) by framing it as a decision advisor for harvest management.

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?

The description provides context ('what should I do with my harvest today?') and a caveat ('DEMO model', 'verify prices'), which guides appropriate use. However, it does not explicitly state when to use this tool versus alternatives like price_trend_analysis.

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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