entsoe-mcp

get_series

Generic time-series query for ANY registered series endpoint.

One tool covers every (non-outage) endpoint in the registry, so adding a new dataset (call list_endpoints() to see the current 14) gets an MCP surface automatically — no new tool to learn.

Argument shape adapts to the endpoint: • single-zone (day_ahead_price, actual_load, generation_per_type, …) → pass zone="DE_LU" • cross-zone (crossborder_flow, scheduled_exchanges, net_transfer_capacity_dayahead) → pass from_zone="DE_LU" AND to_zone="FR" • psr-dependent (generation_per_type, wind_solar_forecast, installed_generation_capacity) → optionally filter via psr_types=["solar","wind_onshore"]

start/end: UTC by default; start inclusive, end EXCLUSIVE (for "all of April 2026" use end=2026-05-01). Pass tz="local" or an IANA name to interpret as wall-clock in that timezone.

aggregation: 'raw' (default — native PT15M/PT60M per endpoint), 'hourly' (AVG over quarters → one row per hour, useful for the growing list of PT15M-stored endpoints like DE_LU day_ahead_price), 'daily', or 'monthly'. For day-ahead prices specifically the auction still clears hourly even where stored at PT15M, so AVG=any-quarter; SUM would 4× over-count.

Outage-family endpoints (different schema) stay on get_outages().

Input Schema

TableJSON Schema

Name	Required	Default
`tz`	No
`end`	Yes
`zone`	No
`start`	Yes
`to_zone`	No
`endpoint`	Yes
`from_zone`	No
`psr_types`	No
`aggregation`	No	raw

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

TDQS

A4.8/5.0

Behavior5/5

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

With no annotations provided, the description carries the full burden and delivers extensively: UTC default, start-inclusive/end-exclusive semantics, tz interpretation, aggregation transformations, and the concrete warning that SUM would 4x over-count PT15M-stored day-ahead prices. This goes well beyond what the input schema alone could communicate.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is long but every section earns its place by explaining a parameter family, boundary condition, or exclusion. Bullet grouping and the day-ahead over-count caveat make it scannable and information-dense without redundancy.

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 9-parameter generic query with no annotations and no schema descriptions, the description is complete enough for correct invocation: it explains required time arguments, endpoint-dependent zone arguments, optional psr filters, aggregation modes, and the outage-family exception. An output schema exists, so return-value details do not need to be repeated in the description.

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

Parameters5/5

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

Schema description coverage is 0%, but the description compensates fully: endpoint, zone, from_zone/to_zone, psr_types, tz, start/end, and aggregation are all explained with examples and behavioral nuance. It even clarifies which parameter shapes apply to which endpoint families.

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 'Generic time-series query for ANY registered series endpoint', naming a concrete verb, resource, and scope. It differentiates itself from sibling tools by framing itself as the registry-wide generic query, and explicitly separates outage-family endpoints by routing them to get_outages().

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?

It gives clear guidance: use for non-outage series endpoints, call list_endpoints() to discover the registered sets, and pass endpoint-specific zone/psr argument shapes. It explicitly says outage endpoints belong on get_outages(). However, it does not mention the specialized get_* sibling tools or state when an agent should prefer them over this generic tool, so alternative routing is not fully explicit.

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.

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