Metry

By Metry

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Overview

Say goodbye to manual exports and hello to your energy data, directly in ChatGPT. Metry is the Nordic energy data platform used by property owners, energy managers, and sustainability teams to collect quality-assured meter data across electricity, heating, cooling, gas, and water - without installing any new hardware. A selection of things you can do with Metry in ChatGPT: - Analyse energy trends and spot anomalies across your portfolio - Compare monthly consumption between buildings or assets - Navigate your organisation structure - from regions down to individual spaces - Pull hourly, daily, or monthly consumption series for any period - Answer sustainability reporting questions without leaving your workflow

Tools

get_asset_consumption

ChatGPT

Fetch monthly consumption for a property/building (asset), split into common/tenant/other area. Granularity is always month and the metric is energy. Returns a compact series: a shared 'buckets' label array plus parallel per-metric value arrays (energy, energy_common_area, energy_tenant_area, energy_other_area).

get_consumption

ChatGPT

Fetch consumption for one or more meters, or a tree node. One id = that meter or tree node; multiple ids = per-meter series; aggregate=true = summed across the ids. Returns a compact series: a shared 'buckets' label array plus parallel per-metric value arrays. Periods are interpreted in the meter's local timezone.

get_current_account

ChatGPT

Return the account object for the authenticated user, as raw JSON. Use this to discover the caller's identity and primary organization.

get_meter

ChatGPT

Return the full backend object for one meter by id, as raw JSON. Use get_meters for a lean list.

get_meters

ChatGPT

Search and page the authenticated organization's meters. Defaults to box=active (the meters that actually have consumption data) and to 50 meters per page. Page with 'offset' rather than by raising 'limit'. For a broad 'what does this org look like' question call get_organization_overview instead - it answers in one small response. Use get_meter for the full object of a single meter.

get_organization_overview

ChatGPT

Answer 'what does this organization look like' in one small response: total meters, the split by box and by meter type, and the tree's root nodes one level deep. Start here for any broad question about the organization - it costs a fixed amount regardless of how many meters the client has. Use get_meters to list or search actual meters, and get_organization_structure with 'node_id' to expand a branch of the tree.

get_organization_structure

ChatGPT

Expand part of the organization's tree: how it groups meters into regions, properties, buildings and spaces. Returns the roots by default, or the one node named by 'node_id', one level deep. Drill down by calling again with a child's id as 'node_id'. Not every meter is filed in the tree, so a node's counts are not the organization's meter total - use get_organization_overview for that. Node property/building/space detail comes from get_tree_node.

get_tree_node

ChatGPT

Return the full backend object for one tree node by id, as raw JSON. This is the only way to reach a node's property, building or space detail (address, area, sector, energy class, designation), which get_organization_structure omits.

get_asset_consumption

Claude

get_consumption

Claude

get_current_account

Claude

get_meter

Claude

get_meters

Claude

get_organization_overview

Claude

get_organization_structure

Claude

get_tree_node

Claude

Capabilities

View Portfolio OverviewNavigate Your Organisation Structure, From Regions Down To Individual SpacesSearch MetersRead Meter And Building DetailsPull Hourly, Daily Or Monthly Consumption Series For Any PeriodCompare Monthly Consumption Between Buildings Or AssetsAnalyse Energy Trends And Spot Anomalies Across Your PortfolioAnswer Sustainability Reporting Questions Using Your Own Consumption Data

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