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supply-chain-intelligence

by io.github.SupplyMaven-SCR

get_energy_forecast

Get the US Energy Information Administration's Short-Term Energy Outlook (STEO) — official government forecasts for energy production, consumption, and pricing. Returns both historical actuals and forward-looking projections for crude oil prices, natural gas prices, electricity generation, renewable energy production, and petroleum consumption. The STEO is the most widely referenced energy forecast in the world. Distinguishes actual historical data from projected forecasts using the isActual flag. Used by energy traders, logistics companies budgeting fuel costs, and macro analysts.

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

A4.4/5.0
Behavior4/5

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

With no annotations, the description takes on full transparency burden. It discloses that the tool returns both historical actuals and projections, and mentions the isActual flag for distinguishing them. It does not discuss response shape or rate limits, but for a zero-parameter read-only tool, it covers the key behavioral aspects well.

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 a single paragraph with 4-5 sentences, each providing meaningful content. It could trim the promotional line 'most widely referenced' but otherwise is appropriately concise and front-loaded with the core purpose.

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

Completeness5/5

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

For a tool with no parameters, no output schema, and no annotations, the description fully equips the agent with purpose, data coverage, output differentiation (isActual), and typical use cases. Nothing essential is missing for selecting and invoking this tool correctly.

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 no parameter specifics because there are none. It appropriately focuses on the data content rather than 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 specific resource (US EIA STEO) and the action (get forecast), listing the exact content areas (crude oil, natural gas, electricity, renewables, petroleum). It distinguishes itself from sibling tools by naming the unique data source and government 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 implies usage context by naming the target audience (energy traders, logistics companies, macro analysts) but does not explicitly exclude alternative tools or provide comparison. The unique focus on STEO implicitly differentiates it from generic commodity or energy tools.

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