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Glama

Energi Data Dk

by io.github.pipeworx-io

Query Dataset

query_dataset
Read-onlyIdempotent

Generic escape hatch for any of the ~100 Energi Data Service (Energinet, Denmark) datasets. Examples: "ProductionConsumptionSettlement" (production/consumption by type per area+hour), "CO2EmisProg" (CO2 prognosis), "Elspotprices", "CO2Emis". Returns the raw records array for the dataset. Keyless official open data.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
endNoISO date/time upper bound.
sortNoSort expression, e.g. "HourUTC DESC".
limitNoMax records (default 20).
startNoISO date/time lower bound.
filterNoColumn→value[] filter, e.g. {"PriceArea":["DK1"]}. Values are arrays.
datasetYesDataset name, e.g. "ProductionConsumptionSettlement".

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "dataset": "ProductionConsumptionSettlement",
+    "limit": 20
+  },
+  {
+    "dataset": "Elspotprices",
+    "end": "2026-06-02",
+    "filter": {
+      "PriceArea": [
+        "DK1",
+        "DK2"
+      ]
+    },
+    "start": "2026-06-01"
+  }
+]
  2. First observed

TDQS

A4/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, non-destructive behavior. The description adds: 'Returns the raw records array for the dataset. Keyless official open data.' This tells agents it returns raw data and requires no authentication, which is useful context beyond annotations.

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 very concise: three sentences plus examples. It front-loads the core purpose ('generic escape hatch') and lists examples immediately. No unnecessary words.

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?

Given 6 parameters, no output schema, and the tool's generic nature, the description covers the essential behavior (returns raw records, keyless open data). The examples help, but missing details like pagination limits or error behavior. Still, it's largely adequate for an agent to use correctly.

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 coverage is 100%, so the schema already documents parameters. The description adds value through concrete examples showing typical parameter combinations, but does not explain nuances like how filter or sort work beyond the schema.

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 is a generic escape hatch for ~100 Energi Data Service datasets, with concrete examples. It distinguishes itself from specialized sibling tools like spot_prices and co2_intensity by implying it covers all other datasets.

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 calls it a 'Generic escape hatch' which suggests using it when dedicated tools aren't available, but it does not explicitly state when to use or avoid it versus siblings. The examples hint at usage patterns but lack explicit 'when to use' guidance.

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
Disambiguation2/5

Several tools form overlapping clusters that are hard for an agent to distinguish: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, validate_claim, discover_tools, and suggest_questions all cover factual/research lookups, and the five Polymarket tools also overlap heavily. ask_pipeworx_beta is explicitly identical to ask_pipeworx right now, making misselection essentially guaranteed in that pair. The descriptions are detailed, but the boundaries between many tools remain unclear at the set level.

Naming Consistency4/5

Almost all tool names are snake_case and mostly follow readable verb_noun or domain-specific patterns, such as ask_pipeworx*, resolve_entity, validate_claim, and polymarket_*. Minor deviations exist — bare verbs like remember/recall/forget/subscribe and noun-style names like spot_prices/co2_intensity — but there is no mixed casing and the overall pattern is predictable.

Tool Count2/5

34 tools is already over the 25+ threshold for a well-scoped server, and the mismatch is much worse because only three tools (co2_intensity, spot_prices, query_dataset) relate to the advertised Energi Data DK domain. The other 31 tools appear to belong to an unrelated general-purpose Pipeworx platform, so the count is not appropriate for the server's stated purpose.

Completeness3/5

For the stated Energi Data DK domain, energy data access is partially covered: two dedicated tools plus query_dataset as a generic escape hatch for all ~100 datasets prevents hard dead ends, but there are no typed tools for most of those datasets and no energy-specific monitoring/alerting. If the real intended domain is the broader Pipeworx platform, coverage is much stronger, but then the server name is misleading.

Resources