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supply-chain-intelligence

by io.github.SupplyMaven-SCR

supply_chain_risk_assessment

Assess current global supply chain disruption risk. Returns the Global Disruption Index (GDI) — a real-time composite score from 0-100 measuring disruption across transportation, energy, materials, and macroeconomic pillars. Higher scores indicate greater supply chain risk. Built from 200+ live data variables including port congestion at 26 global ports, commodity prices for 31 assets, US border crossing delays, manufacturing output from 8 power grid regions, and Federal Reserve economic indicators. Used by procurement teams, logistics planners, commodity traders, and supply chain managers for real-time supply chain visibility and risk monitoring.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Added

TDQS

A4.3/5.0
Behavior4/5

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

With no annotations provided, the description carries the full burden. It transparently explains the score range (0-100), direction (higher = greater risk), and data inputs (200+ variables, specific sources). This goes beyond the empty schema and gives the agent a solid understanding of expected output and semantics.

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 information-dense but well-structured: it opens with the core purpose, then explains the score and its meaning, lists data sources, and closes with the audience. Every sentence contributes value, with no redundancy or fluff.

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 zero-input tool with no output schema, the description is comprehensive. It fully defines what the tool returns (GDI score), how to interpret it (higher = more risk), and what data it relies on. It leaves little ambiguity about expected behavior or results.

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 has zero parameters, so the baseline is 4. The description correctly focuses on interpreting the output rather than parameter details, which would be irrelevant. No additional parameter info is needed.

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?

Clearly states it assesses global supply chain disruption risk and returns the Global Disruption Index (GDI), a composite score. This distinguishes it from sibling tools like risk_pillar_breakdown by emphasizing the holistic, real-time nature of the metric.

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?

Describes the intended users (procurement, logistics, traders, managers) and use case (real-time visibility and risk monitoring). However, it does not explicitly mention alternatives or conditions when this tool should be preferred over related tools, such as risk_pillar_breakdown for detailed pillar analysis.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All supply-chain-intelligence toolsInspect
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TDQS

A4.1/5.0
Disambiguation5/5

Each tool targets a specific and distinct supply chain intelligence need, such as monitoring port congestion, tracking commodity prices, or analyzing trade policy. While there are multiple tools related to signals and ports, each has a clearly defined purpose (e.g., real-time monitoring vs. trend analysis vs. predictive signals), reducing ambiguity.

Naming Consistency3/5

Most tools use the 'get_' prefix (e.g., get_port_congestion_trends, get_border_delays), but several tools lack it, such as commodity_price_monitor, port_congestion_monitor, and supply_chain_risk_assessment. This mix of 'get_' and non-'get_' naming creates inconsistency. Additionally, some names are noun-heavy (risk_pillar_breakdown) while others are verb-noun (commodity_price_monitor).

Tool Count3/5

At 31 tools, the server is on the higher end of acceptable scope for a comprehensive supply chain intelligence platform. However, some redundancy exists (e.g., multiple signal and port tools), and the count may overwhelm agents without clear prioritization. It is slightly above the ideal range for coherence.

Completeness5/5

The tool set covers the major aspects of external supply chain risk: commodities, transportation (ports, borders, air, rail, chokepoints), manufacturing, macroeconomic indicators, trade policy, natural disasters, and labor actions. It also includes analytical tools like trend analysis and predictive signals, leaving no obvious gaps for its stated purpose of monitoring global supply chain disruptions.

Resources