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manas-katyal

EnergiMCP

by manas-katyal
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TypeScript
Hybrid

Query a dataset

query_dataset
Read-only

Fetch rows or per-column statistics from Danish energy datasets, filtered by time range, columns, and allowed values.

Instructions

Fetch rows from any dataset. start/end filter on the dataset's own time column (see describe_dataset). Keep results small: name the columns you need, and use summary=true to get per-column statistics instead of rows when the range is long. Amounts and units are whatever the dataset documents; this tool does not convert them.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
endNoEnd of the period, excluded. Danish local time. Either a timestamp (2026-01-01 or 2026-01-01T00:00) or a relative expression: now, StartOfDay, StartOfMonth, StartOfYear, optionally with an ISO 8601 offset such as now-P1D, now-PT15M, StartOfYear, now+P1D.
sortNoe.g. 'HourUTC desc' or 'PriceArea,HourUTC'. Defaults to newest first.
limitNoMaximum rows (default 200, or 5000 when summary=true). 0 is rejected here: to this API it means every row ever.
startNoStart of the period, included. Danish local time. Either a timestamp (2026-01-01 or 2026-01-01T00:00) or a relative expression: now, StartOfDay, StartOfMonth, StartOfYear, optionally with an ISO 8601 offset such as now-P1D, now-PT15M, StartOfYear, now+P1D.
filterNoColumn to allowed values, e.g. {"PriceArea": ["DK1", "DK2"]}. Values within a column are OR-ed, columns are AND-ed.
offsetNoRows to skip, for paging through a large result
columnsNoColumns to return. Omitting this returns all of them, which is often far more than you need.
datasetYesExact datasetName from list_datasets
summaryNoReturn min/max/mean/sum per numeric column and the distinct values of category columns, instead of the rows themselves

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already cover readOnly and openWorld hints. The description adds meaningful behavior beyond them: time filtering is against the dataset's own time column, amounts and units are not converted, and summary=true returns per-column statistics instead of rows. This is useful, non-duplicative context.

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?

Three sentences, front-loaded with the core action, followed by the most important usage caveats. Every sentence earns its place, and there is no redundant restatement of the schema.

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?

For a 9-parameter tool with no output schema, the description plus detailed schema covers the main decisions an agent needs to make: dataset selection, time filtering, column selection, and summarized output. It doesn't describe the exact response envelope, but row/statistics semantics are stated clearly enough for selection and invocation.

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?

Schema coverage is 100%, so the baseline is 3. The description adds value by clarifying that start/end refer to the dataset's own time column and that summary=true is a substitute for rows over long ranges. It doesn't over-explain parameters already well documented in the schema.

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 clearly states the tool fetches rows from any dataset, which is a specific action on a clear resource. It doesn't explicitly name sibling tools or contrast itself with get_electricity_prices, but 'any dataset' signals a generic query role versus the specialized sibling tools.

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 gives practical usage context: use start/end on the dataset's own time column, reference describe_dataset, keep results small by selecting columns, and use summary=true for long ranges. It does not explicitly state when to choose this tool over specialized siblings, but the guidance within the tool is clear.

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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