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supply-chain-intelligence

by io.github.SupplyMaven-SCR

port_congestion_monitor

Monitor real-time port congestion and vessel traffic at 26 major global ports. Returns vessel counts at berth and at anchor, congestion score versus historical baseline, and port status. Covers US ports (Los Angeles, Long Beach, Savannah, Houston, New York/New Jersey, Charleston, Oakland, Seattle, Tacoma), Asian ports (Shanghai, Singapore, Busan, Ningbo, Shenzhen, Hong Kong), and European ports (Rotterdam, Hamburg, Antwerp, Felixstowe, Piraeus). Used by freight forwarders, logistics teams, and importers to monitor delays, plan routing, and anticipate lead time changes.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

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

  1. Changed1 schema field changed
    • removedInput schema / $schema
      Removed value: -"http://json-schema.org/draft-07/schema#"
  2. Added

TDQS

A4.1/5.0
Behavior3/5

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

With no annotations, the description carries the full burden. It transparently lists what the tool returns (vessel counts at berth/anchor, congestion score, port status), which is helpful. However, it does not disclose whether this is a snapshot or streaming call, update frequency, or any access-related behaviors. It also doesn't clarify read-only nature, though that may be inferred.

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 organized in four sentences, each with a clear role: purpose, outputs, port coverage, and user applications. It is slightly longer than necessary due to the full port enumeration, but the list is valuable for setting expectations. No filler content is present.

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?

Given the absence of an output schema and annotations, the description does a solid job of explaining what the tool returns and its scope. It lacks details on data freshness, time periods, or interpretation of the congestion score, but the core functionality and coverage are well covered. This is nearly complete for a no-parameter monitoring tool.

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 adds contextual value by enumerating the covered ports, which implicitly tells the user that no input is needed because the scope is fixed. There are no parameter details to explain, so the description appropriately focuses on output and coverage.

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: 'Monitor real-time port congestion and vessel traffic at 26 major global ports.' It specifies the exact resources (ports) and outputs (vessel counts, congestion score, port status). It also distinguishes itself from the sibling 'get_port_congestion_trends' by emphasizing 'real-time' and 'historical baseline' comparison.

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 clear usage context by naming target users (freight forwarders, logistics teams, importers) and use cases (monitor delays, plan routing, anticipate lead time changes). It implies real-time monitoring versus historical trends, but does not explicitly state when NOT to use this tool or name alternatives like get_port_congestion_trends.

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

A4.1/5.0
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