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Bigred97

aemo-mcp

describe_dataset

Describe a NEM dataset by providing its schema, filters, update frequency, and source URL. Get metrics, units, and example invocations.

Instructions

Describe one NEM dataset — schema, filters, cadence, source URL.

Examples: detail = await describe_dataset("dispatch_price") # → filters: [{key: "region", values: ["NSW1", "QLD1", ...]}] # → metrics: {rrp: "$/MWh"} # → cadence: "5 min"

Returns: DatasetDetail with id, name, description, filters, units, source URL, and example invocation strings.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataset_idYesDataset ID like 'dispatch_price', 'generation_scada'. Use search_datasets() to discover, or list_curated() to enumerate. Case-insensitive.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYes
nameYes
descriptionYes
is_curatedYes
cadenceNo
filtersNo
unitsNo
source_urlYes
examplesNo
Behavior4/5

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

With no annotations, the description carries full burden for behavioral disclosure. It clearly states the tool returns a DatasetDetail with specific fields (id, name, description, filters, units, source URL) and is read-only. This adequately informs the agent of the tool's behavior.

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 concise, front-loaded with purpose, and uses bullet-style examples. Every sentence adds value, though it could be slightly tighter without the example code block.

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?

Given a single required parameter and an output schema (mentioned), the description fully covers what the agent needs: input example, output fields, and relationship to search_datasets for discovery. No gaps remain.

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?

The input schema already includes a detailed description and examples for the single parameter (dataset_id). Schema description coverage is 100%, so the description adds limited additional semantic value beyond reinforcing the dataset vocabulary.

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 explicitly states the tool's action ('Describe one NEM dataset') and the specific aspects covered (schema, filters, cadence, source URL), clearly distinguishing it from sibling tools like get_data (which likely returns data) or search_datasets (which discovers datasets).

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 provides an example usage and lists return fields, but does not explicitly contrast with alternatives or state when not to use. The sibling context implies differentiation, but direct exclusions are absent.

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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