voltcast

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

No annotations are provided, so the description carries the full burden. It clarifies the tool is a query over prices and forecast but does not disclose whether it modifies data, any authentication requirements, rate limits, or how results are structured. The behavioral impact is assumed read-only but not stated.

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?

The single sentence is front-loaded with the core purpose, making it efficient and scannable. The mention of 'Pro+' is slightly extraneous but not harmful. Overall, it is appropriately concise.

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?

With 5 parameters, no output schema, and no annotations, the description lacks detail on return format (what constitutes a 'window'), how to interpret results, and any prerequisites (e.g., which zones are supported). It is insufficient for an agent to confidently use the tool without external knowledge.

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 coverage is low (40%) with only `count` and `duration_minutes` having descriptions. The tool description adds no parameter-specific details beyond the general purpose, leaving the meaning of `zone`, `from`, and `to` unexplained. The description fails to compensate for the schema gaps.

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 clearly states the tool's action ('Find'), the resource ('cheapest contiguous time windows'), and the context ('to run a load, over published prices + forecast'). It distinguishes itself from siblings like `get_prices` and `get_forecast` by combining both data sources to identify optimal windows.

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?

Usage context is implied ('to run a load') but the description lacks explicit guidance on when to use this tool versus alternatives like `get_prices` or `get_forecast`. It does not provide exclusion criteria or mention conditions where this tool is inappropriate.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

No annotations provided; description only states 'derived from the live generation mix' but does not disclose behaviors such as rate limits, data freshness, or what happens with missing data. The tool could include historical data but that is not clarified.

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?

The description is a single sentence that front-loads the key outputs, but it lacks space for important context like time range handling. It is concise but at the expense of completeness.

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?

Given no output schema and 3 undocumented parameters, the description should explain return format and parameter usage. It only gives output types without structure, and omits required parameter (zone) guidance.

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 fails to explain any of the three parameters (to, from, zone). It does not define zone values or time formats, leaving the agent to guess.

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 clearly states the tool returns carbon intensity and green score, distinguishing it from siblings like get_prices or get_renewables. However, it does not explicitly mention that it can provide historical data (given to/from params) or emphasize derivation from live mix.

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?

No guidance on when to use this tool versus alternatives like get_renewables or list_zones. The agent is not told if historical or forecast data is available, nor any prerequisites like zone format.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

No annotations are provided, so the description must fully disclose behavior. It mentions the output (P50/P90) and availability (up to 7 days) but fails to discuss prerequisites, idempotency, rate limits, or whether authentication is required.

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?

The description is a single concise sentence that conveys key information. It is appropriately front-loaded but could benefit from breaking into multiple sentences for clarity.

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?

Without an output schema, the description should specify return format, pagination, or data structure. It only mentions P50/P90 curves and a link to accuracy stats, leaving major gaps for tool invocation.

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?

The schema has 0% description coverage, and the description does not explain the 'zone' or 'horizon' parameters. Their meaning, format, or defaults are omitted, leaving the agent with no guidance beyond their types.

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 clearly states it provides a probabilistic price forecast (P50 curve; P10/P90 on Pro+) up to 7 days ahead. This distinguishes it from sibling tools like 'get_prices' (likely real-time) and 'cheapest_window' (optimization).

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?

The description implies use for probabilistic forecasts but does not provide explicit when-to-use or when-not-to-use guidance. No alternatives are mentioned, though sibling names offer some context.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

With no annotations, the description provides useful behavioral context (units, resolution, API requirement) but omits details about output format, error handling, or supported data ranges beyond defaults.

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?

Two succinct sentences cover core purpose and a key prerequisite, with no redundant 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?

Given no output schema, the description lacks details about return structure (e.g., time series format). While adequate for a simple tool, it could better address output expectations.

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 coverage is 75%, and the description adds value by clarifying that 'native' resolution means 15-minute intervals and providing an example zone code ('DE-LU'), building on the schema's descriptions.

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 clearly states the tool retrieves day-ahead electricity prices in EUR/MWh with 15-minute native resolution for a bidding zone, distinguishing it from siblings (e.g., cheapest_window, get_carbon).

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?

The description mentions the API key prerequisite but does not explicitly state when to use this tool versus alternatives like cheapest_window or get_forecast. Usage context is implied by the tool's name.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

With no annotations, the description carries the burden. It transparently reveals the tool provides day-ahead forecasts, multiple models, confidence intervals, realized data, and verification. This gives a clear picture of what the tool does and its output complexity.

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?

The description is a single long sentence that packs many details. While informative, it could be structured more clearly (e.g., bullet points). It is not excessively long but loses some readability.

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?

Given the tool's complexity and absence of an output schema, the description covers key output components: forecasts, realized data, bands, and verification. However, it does not explain the 'Pro/Scale/Balancing/Quant' tags or output format, leaving minor gaps.

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?

The description does not elaborate on parameters beyond what the schema provides. Schema coverage is 100% with basic descriptions (ISO 8601 dates, zone code). The term 'Day-ahead' hints at the timeframe but does not clarify how to/from parameters control it.

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 clearly states the tool retrieves day-ahead wind and solar generation forecasts, including multiple models and realized generation. It specifies precise content like TSO forecast, Voltcast model with q10-q90 bands, and verification. This distinguishes it from sibling tools like get_prices or get_carbon.

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?

The description implies usage for renewable generation data but does not explicitly state when to use this tool over alternatives (e.g., get_forecast). No guidance on prerequisites, exclusions, or typical scenarios is provided.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

With no annotations, the description carries full burden. It discloses that no authentication is required, but does not mention other behavioral aspects like data freshness or response structure. Adequate for a simple list 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?

The description is a single sentence, front-loaded with the core purpose, and every word contributes value. No unnecessary 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?

Given zero parameters, no output schema, and a simple function, the description covers purpose and a key behavior. Lack of output structure details is a minor gap, but overall complete for the tool's complexity.

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?

There are zero parameters, so schema coverage is 100%. The description adds no parameter-level semantics, which is acceptable per baseline guidance (0 params = baseline 4).

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 clearly states the verb 'list' and the resource 'supported European bidding zones' with details on what is returned (codes, names, capabilities). It distinguishes itself from sibling tools that fetch data (e.g., get_prices, get_forecast).

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?

The description mentions 'No auth required' but does not explicitly state when to use this tool versus alternatives. Usage context is implied but not detailed.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.