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supply-chain-intelligence

by io.github.SupplyMaven-SCR

get_predictive_signals

Statistically validated leading indicator signals evaluated against live supply chain data. Each signal is a Granger-causal relationship tested at p<=0.01 with directional accuracy >=55%. Signals predict commodity price movements, manufacturing shifts, and macroeconomic changes 1 week to 6 months ahead. Returns ACTIVE (threshold crossed — act now), WATCH (approaching threshold — prepare), or CLEAR status for each signal. 58 signals across 3 tiers organized by predictor group (GDI pillars, SMI regions, cross-index spreads). Used by commodity traders for forward-looking positioning, procurement teams for buy/defer timing, and hedge funds for alternative data signals.

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

A4.5/5.0
Behavior5/5

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

With no annotations, the description carries full burden. It discloses statistical validation (p<=0.01, directional accuracy >=55%), prediction horizon (1 week to 6 months), status categories (ACTIVE, WATCH, CLEAR), and signal count/organization (58 signals, 3 tiers). This is rich operational detail beyond a simple retrieval statement.

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?

The description is detailed and well-structured, starting with the core purpose and then providing methodological and usage context. It is somewhat lengthy but each sentence contributes useful information for an agent deciding to invoke it.

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?

For a zero-parameter tool without an output schema, the description covers the return statuses, prediction targets, and target users. It does not define the exact '3 tiers' or list predictor groups in detail, but overall it is sufficiently complete for invocation.

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, and the schema is empty (coverage 100%). The description adds value by explaining what the returned signal data contains, but no parameter-specific semantics are needed since there are none.

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 the tool returns 'Statistically validated leading indicator signals' and specifies what they predict (commodity price movements, manufacturing shifts, macroeconomic changes) and the statuses returned. It distinguishes itself from siblings by emphasizing Granger-causal validation and forward-looking predictive nature.

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 identifies specific use cases and target users: commodity traders for positioning, procurement teams for buy/defer timing, hedge funds for alternative data. It does not explicitly contrast with alternative tools or state when not to use, but the context is clear.

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