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SupplyMaven MCP Server

by com.supplymaven

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

TDQS

A4.3/5.0
Behavior4/5

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

With no annotations, the description carries the full burden. It discloses the return statuses, live data evaluation, and statistical validation, effectively communicating that this is a read-only data retrieval tool with no destructive side effects.

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 efficiently written and front-loaded with the core value proposition. Each sentence adds meaningful detail, though the density could be slightly improved with structured bullet points for clarity.

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 no output schema, the description explains the key return values (statuses) and provides a broad overview of the content. It doesn't enumerate every field but is sufficiently complete for an agent to understand what the tool offers.

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 per the rubric. The description adds context about the full dataset (58 signals, 3 tiers, predictor groups) without needing to explain parameter syntax.

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 with ACTIVE/WATCH/CLEAR statuses. It distinguishes from siblings like get_action_signals by emphasizing Granger-causal relationships and specific accuracy thresholds.

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?

Provides clear user context (commodity traders, procurement teams, hedge funds) and use cases, implying when this tool is appropriate. It doesn't explicitly name alternatives or exclusions, but the context is unambiguous.

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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TDQS

A4/5.0
Disambiguation4/5

Most tools have distinct purposes targeting specific supply chain dimensions like commodity prices, port congestion, or manufacturing indicators, with clear boundaries. However, some overlap exists between tools like 'get_commodity_volatility_alerts' and 'commodity_price_monitor', which both focus on commodity price changes, potentially causing confusion in tool selection.

Naming Consistency4/5

Tool names follow a consistent 'verb_noun' pattern (e.g., 'get_action_signals', 'get_air_cargo_disruptions'), with minor deviations like 'commodity_price_monitor' and 'manufacturing_output_indicator' using noun-based naming. This maintains readability but slightly breaks the overall convention.

Tool Count2/5

With 25 tools, the count feels excessive for a single server, likely overwhelming users and agents. The server covers a broad domain, but many tools could be consolidated (e.g., multiple commodity-related tools) to reduce complexity and improve focus.

Completeness5/5

The tool set provides comprehensive coverage of the supply chain domain, including risk assessment, real-time monitoring, predictive analytics, and executive reporting. It supports full lifecycle management from data retrieval to actionable insights, with no obvious gaps in functionality.

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