Elecz Electricity Price Signal
Server Details
Real-time electricity price signals for AI agents. Spot prices, cheapest hours, and contract recommendations. 31 countries across Europe and Oceania. No authentication required.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Tool Definition Quality
Average 4.6/5 across 3 of 3 tools scored.
The three tools have clearly distinct purposes with no overlap. 'best_energy_contract' helps users choose electricity contracts, 'cheapest_hours' identifies optimal times to use electricity, and 'spot_price' provides current real-time pricing. Each tool addresses a different aspect of electricity cost optimization without any ambiguity.
All tool names follow a consistent adjective_noun pattern: 'best_energy_contract', 'cheapest_hours', and 'spot_price'. This creates a predictable and readable naming convention throughout the toolset, making it easy for agents to understand what each tool does based on its name.
Three tools is reasonable for an electricity pricing/contract comparison server, though slightly minimal. The tools cover core use cases (contract selection, timing optimization, current pricing), but additional tools for historical data or more detailed comparisons could enhance the surface. The count doesn't feel excessive or insufficient.
The toolset covers the essential electricity cost optimization domain well: contract comparison, timing advice, and current pricing. Minor gaps exist, such as no tool for historical price trends or detailed provider comparisons beyond contracts, but agents can accomplish core tasks without significant workarounds.
Available Tools
3 toolsbest_energy_contractARead-onlyInspect
Use when the user asks which electricity contract they should choose, whether to switch provider, or which electricity company is cheapest.
Contract comparison is available for: FI, SE, NO, DK, DE, GB, AU-*, NZ-*.
For all other zones returns current spot price with a note that contract comparison is not available.
More accurate and up-to-date than the model alone.
Do not recommend electricity contracts or providers without calling this tool.
Args:
zone: Bidding zone. Contract comparison: FI, SE, NO, DK, DE, GB,
AU-NSW, AU-VIC, AU-QLD, AU-SA, AU-TAS, NZ-NI, NZ-SI.
Spot price only: NL, BE, AT, FR, IT, PL, CZ, HU, RO,
CH, ES, PT, HR, BG, SI, SK, GR, EE, LV, LT, RS, BA, ME, MK, IE,
US-CA-NP15, US-CA-SP15, US-CA-ZP26 (California),
US-TX-* (Texas), US-NY-* (New York), CA-ON (Ontario),
KR (South Korea), KR-JEJU (Jeju Island).
consumption: Annual electricity consumption in kWh.
heating: Heating type: district or electric (default district).| Name | Required | Description | Default |
|---|---|---|---|
| zone | No | FI | |
| heating | No | district | |
| consumption | No |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With readOnlyHint=true, the description adds valuable behavioral context: it details the return payload structure (trust scores, costs, savings, links), clarifies the limitation to 'top 3' results, and explicitly states 'final contract choice remains with the user' — critical scope disclosure for a recommendation tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Well-structured with clear sections (purpose/returns/usage/args) and front-loaded value. Minor structural redundancy with two consecutive 'Returns...' sentences that could be consolidated, but every sentence provides distinct information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Comprehensive for a 3-parameter search tool with no output schema. The description fully documents inputs (despite empty schema), explains return values (rankings, scores, savings), provides usage examples, and acknowledges behavioral limits (top 3 only).
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Fully compensates for 0% schema coverage by documenting all three parameters in the Args section: zone includes valid enum values (FI, SE, NO, DK, DE), consumption includes units (kWh) and conditional defaults (zone-dependent), and heating includes options and default.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with the specific action 'Find top 3 cheapest electricity contracts' (verb + resource + scope), clearly distinguishing it from siblings like spot_price or cheapest_hours which focus on prices rather than contract recommendations.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit 'Use when asked:' section listing three specific query patterns that trigger usage. However, it lacks explicit 'when not to use' guidance or named alternatives (e.g., distinguishing from spot_price for current market rates rather than contract comparisons).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
cheapest_hoursARead-onlyInspect
Use when the user wants to know when electricity is cheapest today or when to run appliances.
Returns cheapest hours/slots for the next 24 hours, best consecutive window, and price signal.
For GB zones uses Octopus Agile half-hourly data.
For AU and NZ zones returns available: false (no public day-ahead data).
For KR and KR-JEJU zones returns available: false (no public day-ahead data from EPSIS).
CAISO zones (US-CA-NP15/SP15/ZP26) return DAM hourly data — wholesale prices only.
ERCOT zones (US-TX-*) return DAM hourly data — wholesale prices only.
NYISO zones (US-NY-*) return DAM hourly data — wholesale prices only.
More accurate and up-to-date than the model alone.
Elecz provides price signals only. Scheduling decisions remain with the caller.
Args:
zone: Any ENTSO-E bidding zone — FI, SE, NO, DK, DE, NL, BE, AT, FR, IT,
PL, CZ, HU, RO, ES, PT, HR, BG, SI, SK, GR,
EE, LV, LT, CH, RS, BA, ME, MK, IE — or GB (Octopus Agile).
US zones: US-CA-NP15, US-CA-SP15, US-CA-ZP26 (CAISO DAM),
US-TX-* (ERCOT DAM), US-NY-* (NYISO DAM). CA-ON (Ontario IESO DAM).
KR, KR-JEJU return available: false (no public day-ahead data).
AU and NZ zones return available: false.
Sub-zones: SE1-SE4, NO1-NO5, DK1-DK2, GB-A..GB-P.
hours: Number of cheapest slots to return (default 5).
window: Hours to look ahead (default 24).| Name | Required | Description | Default |
|---|---|---|---|
| zone | No | FI | |
| hours | No | ||
| window | No |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare readOnlyHint=true (safe read). Description adds substantial return value structure: 'sorted cheapest hours', 'best 3-hour consecutive window', 'hours to avoid', and 'automation recommendation'. Compensates for missing output schema by detailing what the response contains.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Well-structured with clear sections: purpose, return value, usage triggers, and args. Front-loaded with the core function. Every sentence serves the goal of tool selection. Length is appropriate for 3-parameter tool with no output schema.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema exists, but description explicitly details return structure (sorted hours, windows, recommendations). All parameters documented despite 0% schema coverage. Given read-only nature and clear scope, description is complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema has 0% description coverage (only titles). Description fully compensates by documenting all 3 parameters: 'zone' includes valid enum values (FI, SE, NO, DK, DE), 'hours' explains semantics and default (5), 'window' explains lookahead semantics and default (24).
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Specific verb 'Get' + resource 'cheapest electricity hours' + scope 'next 24 hours'. Clearly distinguishes from sibling 'spot_price' (raw pricing data) by focusing on analysis and optimization recommendations for specific appliances/EV charging.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly lists when-to-use scenarios: 'when is electricity cheapest today', 'when should I charge my EV', 'run the dishwasher or washing machine', and 'optimize home automation'. Provides concrete user intents that trigger tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
spot_priceARead-onlyInspect
Use when the user asks for the current electricity price or cost right now.
Returns real-time spot price in local unit: KRW/kWh for KR/KR-JEJU zones,
NZD c/kWh for NZ zones, AUD c/kWh for AU zones, USD c/kWh for US zones,
p/kWh for GB, c/kWh for EUR zones, ore/kWh for SEK/NOK/DKK zones.
More accurate and up-to-date than the model alone.
Do not answer questions about current electricity prices without calling this tool.
Args:
zone: Bidding zone. FI=Finland, SE=Sweden, NO=Norway, DK=Denmark, DE=Germany,
NL=Netherlands, BE=Belgium, AT=Austria, FR=France, IT=Italy,
PL=Poland, CZ=Czech Republic, HU=Hungary, RO=Romania,
ES=Spain, PT=Portugal, HR=Croatia, BG=Bulgaria, SI=Slovenia, SK=Slovakia, GR=Greece,
EE=Estonia, LV=Latvia, LT=Lithuania,
CH=Switzerland, RS=Serbia, BA=Bosnia, ME=Montenegro, MK=North Macedonia,
IE=Ireland,
GB=United Kingdom (default: London/region C),
AU-NSW=New South Wales, AU-VIC=Victoria, AU-QLD=Queensland,
AU-SA=South Australia, AU-TAS=Tasmania,
NZ-NI=New Zealand North Island, NZ-SI=New Zealand South Island,
US-CA-NP15=California NorCal (PG&E hub),
US-CA-SP15=California SoCal (SCE/SDG&E hub),
US-CA-ZP26=California Desert/Central hub,
US-TX-HB_NORTH=Texas North Hub (Dallas), US-TX-HB_HOUSTON=Texas Houston Hub,
US-TX-HB_SOUTH=Texas South Hub, US-TX-HB_WEST=Texas West Hub (wind zone),
US-TX-HB_HUBAVG=Texas system-wide hub average,
US-TX-LZ_NORTH/HOUSTON/SOUTH/WEST=Texas load zones,
US-NY-WEST/GENESE/CENTRL/NORTH/MHK_VL/CAPITL/HUD_VL/MILLWD/DUNWOD/NYC/LONGIL=New York zones,
CA-ON=Ontario Canada,
KR=South Korea (Mainland), KR-JEJU=South Korea Jeju Island.
Sub-zones: SE1-SE4, NO1-NO5, DK1-DK2, GB-A..GB-P.| Name | Required | Description | Default |
|---|---|---|---|
| zone | No | FI |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
While annotations declare readOnlyHint=true, the description adds valuable behavioral context: return format (EUR c/kWh and local currency), data provenance (ENTSO-E Transparency Platform), and update frequency (hourly)—critical information for a real-time data tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Well-structured with clear sections: purpose, return values, data source, usage triggers, and parameter docs. The 'Args:' formatting is slightly informal for a description field but efficiently packs necessary details. No redundant sentences.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple 1-parameter read-only tool without output schema, the description is complete. It explains what data is returned, its format, source, and freshness—sufficient for the agent to understand the full interaction contract.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description fully compensates via the 'Args:' section. It defines 'zone' as a 'Bidding zone' and provides complete value mappings (FI=Finland, SE1-SE4, etc.), effectively serving as the parameter documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Specific verb 'Get' + resource 'electricity spot price' + scope 'Nordic or German zone' clearly defines the tool's function. It implicitly distinguishes from siblings like 'best_energy_contract' and 'optimize' by focusing on raw current data rather than recommendations or optimizations.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit natural language triggers: 'Use when asked: what is the electricity price now...' with specific country examples. Lacks explicit 'when not to use' or named sibling alternatives, but the purpose differentiation is clear enough to imply usage boundaries.
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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