Skip to main content
Glama

supply-chain-intelligence

by io.github.SupplyMaven-SCR

get_corridor_risk

Monitor risk levels across 10 major ocean freight trade corridors (China-US West Coast, China-US East Coast, China-Mexico, Taiwan-US, India-US, China-Europe, Europe-US, Middle East-Europe, Brazil-US). Each corridor chains origin ports, chokepoints, and destination ports into a single lane scored by its weakest link (highest risk waypoint). Scores combine real-time port congestion data with active natural disaster proximity. Used by logistics planners for route risk comparison, procurement teams for supply chain exposure assessment, and freight forwarders for disruption early warning.

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, the description explains the scoring methodology (weakest link, combining real-time port congestion and active natural disaster proximity) and the scope of 10 corridors. This goes beyond a simple 'monitor' phrase and gives behavioral insight, though it does not explicitly state the output format or that it is read-only.

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 three sentences long and packs in valuable detail: corridor list, scoring logic, and target users. It is information-dense without being verbose, though the corridor list could be considered slightly niche; still, every sentence earns its place.

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?

Despite lacking an output schema, the description gives sufficient context about the tool's operation and expected use cases. The only minor gap is not specifying the exact return format, but for a zero-parameter monitoring tool, this is acceptable.

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 description has nothing to add beyond the empty input schema. The baseline of 4 applies because parameter documentation is unnecessary, and the description does not incorrectly mention any parameters.

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's function: monitoring risk levels across 10 specific ocean freight corridors. It lists the corridors and explains the scoring mechanism (weakest link, combining port congestion and natural disaster proximity), which distinguishes it from sibling tools like port congestion monitor or natural disaster alerts.

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 explicit use cases for logistics planners, procurement teams, and freight forwarders, indicating clear scenarios for use. However, it does not mention when not to use the tool or name alternative tools, so it falls short of full 5-level guidance.

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
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

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