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entsoe-mcp

by com.entsoemcp

list_derivations

List computed-metric derivations available via get_derivation().

Each derivation is a metric computed on-the-fly from one or more landed endpoints (Tier-2 Parquet). Today: capture_price (monthly VWAP capture price + baseload + capture rate per technology; "capture rate" is the industry-standard name for what the JSON response calls quality_factor), negative_price_hours, residual_load, res_share, emissions (monthly CO2 emissions per fuel using IPCC AR5 lifecycle factors, production-based), and tb_spread (monthly or annual Top-Bottom battery-arbitrage spread TB1/TB2/TB4/TB6 per zone on SDAC market days; accepts zone='all' for every zone in one call).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

TDQS

A4.4/5.0
Behavior4/5

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

No annotations are provided, so the description carries the full behavioral burden. It discloses that derivations are computed on-the-fly from Tier-2 Parquet, defines each derivation, notes the capture_rate/quality_factor alias, and mentions the zone='all' behavior for tb_spread. This is valuable behavioral context beyond the empty schema.

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 dense but front-loaded with the purpose. Every clause provides useful detail about a derivation, though the long run-on listing could have been more scannable as bullet points.

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 parameterless list tool with an output schema, this is complete. It names the purpose, enumerates all currently available derivations, and explains key domain-specific semantics such as aliases and zone='all' behavior. The output schema covers return values, so no return-format description is needed.

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?

This tool has zero parameters and an empty schema with 100% coverage, so there is no parameter meaning to add. The description correctly frames it as a no-argument listing operation.

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 opens with a specific verb and resource: 'List computed-metric derivations available via get_derivation().' It then enumerates the exact derivations, which clearly differentiates it from sibling list tools like list_endpoints or list_zones and from the compute-oriented get_derivation tool.

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 phrasing 'available via get_derivation()' signals that this tool is the discovery entry point while get_derivation performs the actual computation. It gives clear contextual guidance but does not explicitly state when not to use 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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TDQS

A3.9/5.0
Disambiguation3/5

Several dedicated getters (get_day_ahead_prices, get_generation, get_load, get_crossborder_flow) overlap with the generic get_series, which explicitly covers the same endpoint families, creating some choice ambiguity. However, the tool names and cross-references like get_derivation vs get_tb_spread and compare_zones vs get_series help an agent distinguish the specific purpose of each tool. Overall there is real overlap, but the descriptions mostly steer an agent correctly.

Naming Consistency5/5

Tool names are lowercase snake_case and consistently follow verb-first patterns: get_ for data retrieval, list_ for reference/discovery, and compare_/data_ for cross-cutting utilities. No mixed casing or erratic verb styles appear. data_coverage is a minor pattern deviation but still reads naturally alongside the other names.

Tool Count5/5

Fourteen tools is well within the appropriate range for an ENTSO-E data platform: several list_ discovery tools, specialized getters, a generic get_series to prevent endpoint-specific tool explosion, plus data_coverage and compare_zones. The count feels comprehensive without being bloated, and the generic query tool keeps the surface scalable.

Completeness5/5

The tool surface covers the core read-only ENTSO-E workflows: endpoint discovery, zone/psr_type reference data, raw time-series via get_series, outages, cross-border flows, load, generation, prices, and server-side derivations. It also adds operational safeguards like data_coverage and analytical shortcuts like compare_zones and get_derivation. There are no obvious dead ends or missing lifecycle stages for the stated domain.

Resources