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get_terna_data

Retrieve Italian electricity generation, demand, exchanges, and zonal market prices from Terna. Specify date and market zone for filtered results.

Instructions

Italian electricity from Terna: generation, demand, exchanges, zonal prices. NORD/CNOR/CSUD/SUD/SICI/SARD zones.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dateNoDate in YYYY-MM-DD format. Defaults to today.
zoneNoItalian market zone: NORD, CNOR, CSUD, SUD, SICI, SARD, or ITALY (national). Defaults to ITALY.
datasetYes"generation" = Italian electricity generation by source (MW). "demand" = Italian electricity demand (MW). "exchanges" = cross-border exchange flows with neighbours. "market_data" = Italian zonal electricity market data.
Behavior3/5

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

With no annotations provided, the description carries the burden of disclosing behavior. It lists data categories and zones, but does not mention units, response format, pagination, or any access constraints. Since this is a read-only data retrieval tool, the risk is low, but the absence of either annotations or explicit behavioral details keeps this at a neutral 3.

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 a single concise sentence, front-loaded with the tool's purpose ('Italian electricity from Terna'), followed by a list of data types and zones. Every word earns its place, with no redundancy or filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool has 3 parameters, one required, and no output schema. The description gives a valuable overview but does not specify return structure, units, or formatting. Since the data types are listed but not explained in detail, completeness is only moderate for an agent need to understand what exactly will be returned.

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 schema already provides 100% coverage with descriptions for all three parameters. The description adds minor context by listing zone names and data categories, which loosely maps to the parameters but does not go beyond the schema in any significant way. Baseline of 3 is appropriate.

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 identifies the resource as Italian electricity data from Terna, listing specific data types (generation, demand, exchanges, zonal prices) and zones. This distinguishes it from sibling tools like get_rte_france or get_smard_data, though it lacks an explicit verb like 'retrieves' or 'provides'.

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 context is clear: this tool is for Italian electricity data from Terna. It implies when to use it (if you need Italian grid data) without needing to reference alternatives. However, it does not explicitly state when not to use it or mention similar tools, so it stops short of a full 5.

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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