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NORTH7 Finance Trading Signals & Stock Market Intelligence

get_commodity_signals

Returns risk scores 0-100 and supply chain disruption alerts for 15 commodities: oil, gold, wheat, coffee, sugar, cotton, corn, natural gas, silver, copper, cocoa, soybeans, platinum, palladium, and lumber. Includes directional assessment (bullish/bearish), seasonal patterns, and AI reasoning per commodity. Updated daily. JSON format. Costs 2 credits per call.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.8/5.0
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 reasonably well: it discloses freshness (daily update), cost (2 credits per call), response format (JSON), and the substance of each record (risk score, alerts, directional assessment, seasonal pattern, AI reasoning). It omits any statement of read-only safety, auth requirements, or rate limits beyond cost, so it is strong but not complete.

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 the return content and payload size, with cost and freshness trailing. The 15-item commodity enumeration is long but functions as a genuine scope boundary. Slightly dense as a single run of sentences, but no filler.

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 and no annotations, so the description must describe the return payload itself, which it does: score range, alert type, directional call, seasonal patterns, AI reasoning. Freshness and cost are covered. Missing coverage is minor: no geographic/exchange scope, and no note on whether the payload is a single object or per-commodity list.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool takes zero parameters, which sets the baseline at 4. There is nothing for the description to clarify on the input side, and it correctly spends no words on parameters.

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?

Specific verb ('Returns') plus a precisely bounded resource: risk scores 0-100 and disruption alerts, with the exact 15 commodities enumerated. That scope statement lets an agent tell it apart from get_prices or get_trading_signals by content. It falls short of 5 only because it never name-checks a sibling tool to route the agent explicitly.

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?

No when-to-use guidance and no named alternative among the twelve siblings, so the agent must infer usage from the resource name. The 'Updated daily' and 'Costs 2 credits per call' notes are decision-relevant constraints that partially compensate, giving implied rather than explicit guidance.

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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