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supply-chain-intelligence

by io.github.SupplyMaven-SCR

get_rail_freight_status

Get US freight rail performance metrics including average train speed, terminal dwell time, cars on line, trains held per day, railcars not moved within 48 hours, total carloadings, intermodal units, and grain transport rates. Sourced from the Surface Transportation Board railroad service metrics, Association of American Railroads carloading data, and USDA grain transportation reports. When rail slows down, inland supply chains back up within days — this data provides early warning of freight bottlenecks across the US rail network.

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

A3.6/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden. It discloses data sources and contextual value, but does not disclose behavioral traits such as whether the data is real-time or historical, any rate limits, response format, or whether it is a read-only operation. This is a significant gap.

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 informative but slightly long due to the exhaustive metric list. Still, every sentence contributes: the first states purpose, the second provides provenance, and the third gives usage context. It is front-loaded with the core action.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description lists many metrics but does not explain the output format, temporal scope (snapshot vs. trend), units, or region granularity. Since there is no output schema, this leaves the agent uncertain about the exact structure of the response. Adequate but with clear gaps.

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 value by listing the metrics returned and the sources, giving the agent a clear understanding of what data will be provided without needing parameter explanations.

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 it retrieves US freight rail performance metrics, enumerating specific data points like average train speed, terminal dwell time, and grain transport rates. This distinguishes it from sibling tools by focusing on rail-specific metrics and naming relevant data sources.

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?

The description implies usage for monitoring rail-related supply chain bottlenecks ('When rail slows down... this data provides early warning'). However, it does not explicitly compare to sibling tools or state when not to use it, leaving some ambiguity about 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