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Bigred97

aemo-mcp

get_data

Retrieve observations from AEMO NEM datasets like dispatch prices, generation, and interconnector flows using filters and optional time windows.

Instructions

Query an AEMO NEM dataset and return observations.

Examples: # Latest NSW dispatch price (preferred over latest() if you want # a window) resp = await get_data("dispatch_price", filters={"region": "NSW1"})

# Whole-day NSW dispatch price for a specific day
resp = await get_data(
    "dispatch_price",
    filters={"region": "NSW1"},
    start_period="2026-05-13",
    end_period="2026-05-13"
)

# Generation by fuel for QLD, current
resp = await get_data("generation_scada", filters={"region": "QLD1"})

# All 6 interconnectors right now
resp = await get_data("interconnector_flows")

Returns: DataResponse with records, units, period bounds, NEMWEB source URL, and AEMO attribution.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataset_idYesDataset ID like 'dispatch_price'. Use search_datasets() to discover.
filtersNoDict of filter key → value(s). Common filters: 'region' (NSW1/QLD1/SA1/TAS1/VIC1), 'interconnector' (V-SA/Basslink/...), 'duid' (unit ID), 'fuel' (black_coal/gas/wind/solar/battery/...). Call describe_dataset(dataset_id) to see the valid filters.
start_periodNoInclusive start of the period window in AEMO market time (UTC+10). Accepts 'YYYY', 'YYYY-MM', 'YYYY-MM-DD', or 'YYYY-MM-DD HH:MM'. Defaults to None which fetches just the most recent NEMWEB file for the dataset.
end_periodNoInclusive end. Same format as start_period.
formatNoResponse shape. 'records' (default): flat list of observations. 'series': observations grouped by dimensions. 'csv': returns the result as a CSV string in the `csv` field.records

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataset_idYes
dataset_nameYes
queryNo
interval_startNo
interval_endNo
unitNo
recordsNo
csvNo
sourceNoAustralian Energy Market Operator
attributionNoSource: Australian Energy Market Operator (AEMO), NEMWEB. Used under AEMO's Copyright Permissions (general permission for any purpose with accurate attribution). https://aemo.com.au/privacy-and-legal-notices/copyright-permissions
source_urlYes
retrieved_atYes
staleNo
server_versionNo
Behavior4/5

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

With no annotations, the description carries full burden. It explains the response structure (DataResponse with records, units, period bounds, source URL, attribution) and that default start/end fetches the most recent file. It's transparent about behavior, though lacks explicit mention of rate limits or error handling.

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 well-structured: purpose sentence, then examples, then return info. It's front-loaded, every sentence adds value, and the examples are concise yet informative. No wasted words.

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 the output schema exists, the description completes the picture by explaining return values. It references sibling tools for discovery, covers parameter details, and provides comprehensive examples. All necessary context for correct invocation is present.

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% with detailed descriptions. The description adds value by showing example filter combos, explaining the default for start_period, and the format parameter's effect. This goes beyond the schema's definitions, enhancing understanding.

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 'Query an AEMO NEM dataset and return observations.' It provides numerous examples showing specific use cases, differentiating from sibling tools like 'latest' by highlighting window queries. Specific verb+resource+scope ensures the agent understands exactly what the tool does.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly says 'preferred over latest() if you want a window', giving when-to-use guidance. Examples cover various filters and time periods. It references sibling tools 'search_datasets()' and 'describe_dataset()' for discovery, providing clear context for usage and 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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