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Eu Energy Imports

by io.github.pipeworx-io
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Russia Import Trend

russia_import_trend
Read-onlyIdempotent

Year-by-year Russian share of a country's (or the EU's) imports of natural gas, crude oil or coal: Russian volume, total imports and the percentage, for each year. Shows the collapse after 2022 -- e.g. Germany's Russian gas share by year. Source: Eurostat energy statistics.

Example tool call

This call uses example arguments supplied in the tool’s input schema.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "geo": "DE",
      "fuel": "gas",
      "from_year": 2019
    },
    "name": "russia_import_trend"
  }
}

Input Schema

NameRequiredDescriptionDefault
geoNo2-letter country code or "EU". Default "DE".
fuelYesFuel: gas, oil (crude) or coal
to_yearNoLast year (default: latest published)
from_yearNoFirst year (default: 8 years back)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.7/5.0
Behavior4/5

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

Annotations already declare read-only, idempotent, open-world, non-destructive behavior, so the description's burden is lower. The description adds useful behavioral context: the data source is Eurostat energy statistics, the result is year-by-year, and it captures the post-2022 collapse. It does not discuss freshness, rate limits, or data completeness, but with full annotation coverage this is a solid 4.

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 front-loaded with the core purpose, followed by a clarifying example and source attribution. At three sentences, every element contributes, though the source and example could be slightly tighter. It is appropriately sized for the tool's complexity.

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 carries the burden of explaining return values, and it does so by naming the three returned dimensions: Russian volume, total imports, and percentage, for each year. The Eurostat source and the example add practical context. Minor gaps remain around units and handling of missing years, but the definition is otherwise complete for correct invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so all four parameters are already documented in the schema, including defaults and the fuel enum. The description reinforces the geo scope ('country's or the EU's') and fuel types, and provides an example, but does not add meaningfully new parameter semantics beyond the schema. Baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb-and-resource combination: year-by-year Russian share of a country's or the EU's imports of natural gas, crude oil, or coal, including volume, total, and percentage. It is clearly distinct from generic siblings like import_dependency_rate or energy_mix because it is explicitly Russia-specific and time-series by fuel. It does not explicitly name alternatives, which keeps it at a strong 4 rather than a 5.

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?

Usage is implied through the Germany gas-share example and the year-by-year framing, but there is no explicit guidance on when to use this tool versus import_dependency_rate, import_origins, or energy_mix. An agent can infer the appropriate context but is not given exclusions or alternatives.

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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