GridPulse Energy

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

With no annotations provided, the description carries the burden of behavioral disclosure. It reveals that results are 'ranked cleanest first' and provides label thresholds for interpreting carbon intensity levels. This adds meaningful behavior beyond the schema. It does not cover error handling or output format, but for a simple comparison tool, the disclosure is strong.

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 three sentences long, front-loaded with the core purpose, and every sentence adds value: the action, the use case, and the label definitions. There is no redundant or filler content.

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 tool with one simple parameter and no output schema, the description adequately covers the purpose, usage context, and even provides label ranges. It does not specify the exact return format (e.g., a list of zone-intensity pairs), but this is minor given the tool's simplicity. The description is sufficiently complete for an agent to invoke it correctly.

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 description coverage is 100%, so the schema already documents the 'zones' parameter as a comma-separated string with a max of 10. The description adds context that multiple zones are compared and orders results by cleanliness, but it does not introduce new parameter-level details beyond what the schema provides. This aligns with the baseline of 3.

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 function: 'Compare carbon intensity (gCO2/kWh) across multiple zones in one call, ranked cleanest first.' It specifies the resource (carbon intensity), the scope (multiple zones), and the key behavior (ranking). This distinguishes it from sibling tools like compare_green and compare_prices, which target different metrics.

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 gives explicit use cases: 'Use when the user asks which grid is cleanest, or wants to find the lowest-carbon location to run a workload.' This tells the agent when to select this tool. However, it does not mention when not to use it or name alternatives (e.g., get_carbon_intensity for a single zone), so it falls short of a 5.

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 the ranking order (greenest first), the weighting formula (60% renewables, 40% carbon intensity), and the label boundaries, adding meaningful behavioral context beyond a simple comparison.

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 four sentences long, each earning its place: action and ranking, formula, labels, and usage guidance. No filler or repetition.

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 one-parameter tool with no output schema, the description covers the operation, the ranking behavior, the score range, the formula, and the intended usage. It is complete enough for an agent to select and invoke correctly.

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 description for 'zones' already provides full details (comma-separated codes, example, max 10 zones). The tool description adds no extra parameter semantics, so the baseline of 3 for high schema coverage (100%) applies.

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 states 'Compare green scores (0-100) across multiple zones in one call, ranked greenest first,' which specifies the verb (compare), resource (green scores), and scope (multiple zones). It distinguishes itself from siblings like compare_carbon and compare_prices by focusing on the composite green score.

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 states 'Use when the user wants to find the greenest grid for scheduling workloads,' providing clear context. However, it does not name alternative tools or give 'when not to use' guidance, so it doesn't fully meet the 5 threshold.

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 provided, the description carries full burden. It discloses the ranking order (cheapest first), the zone limit, and the currency-group behavior (EU ranked in EUR, US in USD, AU in AUD, GB in GBP). It does not mention edge cases like unavailable zones or errors, but the covered behaviors are meaningful.

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?

Four concise sentences, each providing distinct information: functionality, usage trigger, zone limit, and currency behavior. No redundancy or filler.

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?

The tool has no output schema and no annotations, so the description provides the essential context: what it does, when to use, limits, and currency behavior. It could specify the return fields (e.g., price, rank, currency), but the core purpose is well covered.

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 description coverage is 100%, so the schema already documents both parameters. The description adds context about which currency group each region ranks in, but this is largely derivable from the currency enum. Overall, the description adds modest value beyond the schema.

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 compares current electricity spot prices across multiple zones and returns a ranked list cheapest first. The verb 'compare' and the specific resource 'electricity spot prices' distinguish it from sibling tools like compare_carbon or get_current_price.

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?

It explicitly provides a user query trigger ('which country has the cheapest electricity right now?') and states it is for comparing across regions. It also mentions the 10-zone limit, but does not explicitly state when not to use it or name alternatives like get_current_price for single-zone queries.

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 carries the burden. It discloses that the data is 'current', provides units (gCO2/kWh), and adds interpretive value ('Lower values mean cleaner power'). This goes beyond the basic purpose to explain behavior and meaning.

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 two sentences, front-loaded with the main purpose, and every sentence provides actionable information without waste.

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 simplicity (one parameter, no output schema), the description fully covers what the tool returns, its units, and its intended use cases. It is complete enough for an agent to select and invoke it correctly.

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 100%, with the zone parameter described as 'Bidding zone code (e.g. DE, FR, SE, NO)'. The description itself does not add parameter-level detail beyond the schema, so baseline 3 is appropriate.

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 the current grid electricity carbon intensity for a specific zone in gCO2/kWh, which is a specific verb+resource+scope. It distinguishes from siblings by emphasizing 'for a zone', which contrasts with tools like get_global_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 explicitly mentions use cases: 'use for carbon-aware workload routing or comparing cleanliness across times or regions.' It provides clear context for when to use this tool, though it does not explicitly name alternatives or exclusions.

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 provided, the description carries the full burden of behavioral disclosure. It discloses a non-obvious behavior: 'Calls the API cheapest endpoint, then refines using day-ahead hourly prices so the window respects the given UTC deadline.' It also explains the deadline rollover logic ('if that moment already passed today, tomorrow is used'), which is critical for correct invocation. However, it does not disclose the return format or potential error conditions, so it's not exhaustive.

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 two sentences with no fluff. The first sentence front-loads the core purpose, and the second provides necessary behavioral nuance about deadline handling. Every word earns its place, making it highly concise and well-structured.

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?

The description covers the input semantics and algorithmic behavior well, but without an output schema it should clarify what the tool returns (e.g., the start time, the list of hours, or the total cost). This omission leaves a meaningful gap for an agent needing to interpret the result. Given the tool's moderate complexity and no output schema, the description is adequate but not fully 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 description coverage is 100%, so the baseline is 3. The description adds minimal semantic value beyond the schema: it repeats the 'contiguous' and 'deadline' concepts already present in the property descriptions. The schema already explains the UTC interpretation and rolling behavior, so the description's contribution is largely redundant, meeting but not exceeding the baseline.

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 purpose: 'Find the cheapest N contiguous hours to run a flexible load (EV charging, battery, batch job) before a deadline.' It identifies the specific task, the target resource (flexible load scheduling), and distinguishes itself from sibling tools by emphasizing the deadline and contiguous-hour requirement.

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 context: 'for a flexible load (EV charging, battery, batch job) before a deadline.' This gives clear guidance on when to use the tool, though it does not explicitly name alternatives or exclusions. The absence of direct sibling references is a minor gap, but the context is sufficient for an agent to determine applicability.

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 provided, the description carries the transparency burden. It discloses that results may be 'latest available' rather than real-time, and explains optional extras as bundled 'for the same moment.' It implies read-only behavior via the verb 'Get' but does not discuss rate limits or error behavior. Overall, it provides adequate context beyond the schema.

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 two sentences, front-loaded with the core function. The first sentence states exactly what the tool does, and the second gives usage guidance and optional parameters. No waste, every word contributes.

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 read-only price tool, the description is complete enough: it explains purpose, when to use, parameter meanings, and return value hints (price plus optional carbon/renewables). With no output schema, it cannot detail the full response shape, but it gives the essential context for an agent to use it correctly.

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 description coverage is 100%, so baseline is 3. The description adds semantic value by specifying units for the optional extras (gCO2/kWh, %) and clarifies that they are tied to the same time point as the price. This goes beyond the schema's bare field 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's purpose: 'Get the latest available electricity spot / current market price for one bidding zone (EUR/MWh).' It uses a specific verb and resource, and the emphasis on 'latest available' and 'current' distinguishes it from siblings like get_day_ahead_prices or get_price_history that cover different timeframes.

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 provides explicit usage context: 'Use when the user asks what power costs right now, or needs a fresh price snapshot.' This clearly indicates when to invoke the tool. However, it does not explicitly mention when not to use it or name alternative tools for other scenarios, so it lacks the full when-not/alternatives detail.

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 full transparency burden. It discloses the return format ('Returns a time series in EUR/MWh') and the time horizon ('day-ahead'), which is useful. However, it does not mention any potential side effects, validation behavior, or limitations (e.g., zone availability), leaving some gaps.

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 two sentences with zero fluff. It front-loads the core purpose, then adds usage context and return format. Every sentence earns its place.

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 one-parameter tool with no output schema, the description covers the essential aspects: purpose, usage scenarios, and return format. It could benefit from an explicit note on zone validation, but the schema already handles that, making the description sufficiently 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?

The schema has 100% coverage for the single 'zone' parameter, including guidance to use get_supported_zones if unsure. The description adds little beyond the schema, only restating 'for a zone.' The baseline of 3 is appropriate since the schema does the heavy lifting.

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 core function with a specific verb and resource: 'Get tomorrow's (and near-future) hourly day-ahead electricity prices for a zone.' This distinguishes it from siblings like get_current_price (current prices), get_price_history (historical), and get_cheapest_window (a derived metric).

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 gives concrete use cases: 'typically used to show the full daily price curve, find low-price hours visually, or debug scheduling.' This provides clear context for when to use the tool, though it does not explicitly name alternatives or provide exclusions. The lack of explicit alternatives drops it from a 5, but the context is meaningful.

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 full burden of behavioral disclosure. It adds valuable context beyond the basic function by stating update frequency ('Updated daily'), defining the trend and comparison outputs, and explaining the practical significance. It does not mention error handling or data source limitations, but for a simple read-only lookup, the provided transparency is solid.

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 concise: two sentences with the functional details front-loaded. The second sentence adds contextual value about why gas storage matters, without unnecessary fluff. Every sentence contributes to understanding the tool.

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 low complexity (one parameter, no output schema), the description is largely complete: it explains the core purpose, the outputs (percentage, trend, comparison, risk signal), and update frequency. A minor gap is the lack of explicit output format, but for a simple retrieval tool, the description provides sufficient context for an agent to invoke it effectively.

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 already fully describes the sole parameter 'country' (ISO2 code, EU requirement), so the description adds no additional parameter-level guidance. Per the rubric, high schema coverage yields a baseline score of 3, and the description does not go beyond the schema.

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 function with a specific verb ('Get') and resource ('current natural gas storage level for any EU country'), and details the key outputs (percentage, trend, 5-year comparison, risk signal). It is easily distinguished from sibling tools, which focus on prices, carbon, and weather rather than gas storage.

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 provides clear context for when this tool would be useful, specifically linking gas storage levels to electricity prices. It does not explicitly name alternatives or exclusions, but the context is sufficient for an agent to understand its role among the sibling price and carbon tools.

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 carries the full burden. It clearly explains what data is returned (prices, spread, surplus/deficit) and cites the Elexon balancing mechanism, giving useful behavioral context. It doesn't disclose freshness, units, or potential errors, but for a simple read-only summary the description is fairly transparent.

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 but packs all essential information: resource, key data points, source, and target audience. It is front-loaded with the action and resource, with no filler or repetition. Every clause adds value.

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 annotations, no output schema, and no parameters, the description is the sole source of context. It explains the summary content and intended users, making it sufficient for an agent to decide whether to call it. It could mention return format or units, but for a zero-input summary tool this is quite 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?

There are zero parameters, and the schema already reflects this (100% coverage, no properties). The description doesn't need to add parameter semantics, and per guidelines a zero-parameter tool gets a baseline of 4. The description doesn't add anything unnecessary, so a 4 is appropriate.

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 a Great Britain electricity market summary with specific elements (day-ahead price vs real-time system buy/sell prices, spread, and long/short status). It distinguishes itself from sibling tools by focusing on the Elexon balancing mechanism and GB market specifics, making it unique among price, carbon, and schedule tools.

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 identifies target users ('GB battery storage operators and flexible demand managers') and implies the tool is for obtaining a market imbalance snapshot. It doesn't explicitly name alternatives or state when not to use it, but the context is clear enough for an agent to determine relevance.

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 discloses monthly granularity and global coverage, but lacks details on data source, update frequency, rate limits, or any caveats. This is adequate but has gaps.

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 three sentences, front-loaded with the primary purpose, and every sentence contributes value: what it does, coverage, and usage context. No unnecessary wording.

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 one-parameter tool with no output schema and no annotations, the description provides sufficient context: the metric, unit, granularity, scope, and intended use cases. It doesn't detail the response format, but that is inferable from the described output.

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 description coverage is 100% and already explains the 'country' parameter as ISO2 code with examples. The tool description reiterates this and adds a few more country examples but does not add significant semantic value beyond the schema.

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 monthly carbon intensity (gCO2/kWh) for any country using ISO2 codes, with a specific global scope. It distinguishes itself from sibling tools like get_carbon_intensity by emphasizing worldwide coverage and lists specific countries.

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 explicitly mentions use cases: 'Use for Scope 2 emissions calculations or comparing carbon intensity of electricity globally.' It provides clear context for when to use, but does not explicitly mention exclusions or alternatives, hence not a 5.

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 of behavioral disclosure. It explains the return value (a single 0–100 score) and its interpretation (100 = renewable, 0 = fossil-heavy), which is sufficient for a simple read operation. It does not mention edge cases or error conditions, but these are not critical for this straightforward 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 two sentences long, front-loads the core function ('Single 0–100 green score for a zone'), and provides scale explanation and usage context without unnecessary words or repetition.

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?

Despite having no output schema or annotations, the description fully explains what is returned (score and meaning), why it is useful, and when to use it. For a simple single-parameter tool, this is complete, and the sibling tool list provides additional context without needing explicit mention in the description.

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 100% for the 'zone' parameter, so the schema already provides full parameter documentation. The description adds no additional parameter semantics beyond the schema, so a baseline score of 3 is appropriate.

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 specifies a concrete verb ('get') and resource ('green score for a zone'), and clearly defines the output scale (0–100). It also distinguishes itself from sibling tools by positioning this as a compact signal relative to 'raw carbon + mix', making its purpose unambiguous.

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 gives explicit usage context: 'Use as a compact signal for should I run compute now?' and contrasts with 'raw carbon + mix', implying an alternative. It does not explicitly state when not to use, but the guidance is clear enough for an agent to decide.

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 provided, the description carries full burden. It discloses the core logic (day-ahead prices, green scores, optimization modes, ranked list) but misses behavioral details like read-only nature, handling of missing data, or exact ranking/tie-break behavior.

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 sentences, front-loaded with purpose, zero fluff. Every clause earns its place while covering the essential inputs, methodology, and output type.

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 and no annotations; description says 'ranked list' but doesn't clarify whether the list ranks zones or hour blocks, nor what fields appear in each item. It also doesn't mention behavior when no valid block exists. Missing these details hurts completeness.

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 description coverage is 100%, so baseline is 3. The description adds little beyond the schema; 'contiguous' and 'UTC deadline' are already in the schema. It does clarify the optimization intent but not significantly.

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 a specific verb+resource: 'find the best contiguous hour block before a UTC deadline' across multiple zones. It also distinguishes itself from siblings like get_cheapest_window by supporting multiple optimization modes (price, carbon, balanced).

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 gives a clear use case: 'where flexible compute or charging could run' and 'before a UTC deadline'. It does not explicitly mention alternatives or when not to use, but the context is clear enough.

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 full burden. It discloses the inclusive-range API behavior and the production API plan requirement, which is useful. However, it does not describe the response format, pagination, rate limits, or explicitly state that it is a read-only operation. For a data retrieval tool, this is adequate but not rich.

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 sentences, front-loaded with the core function, followed by use cases and a requirement. No wasted words. Every sentence adds value.

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 3-parameter historical price tool with no output schema, the description covers purpose, usage, and a key prerequisite. It lacks detail on the return shape or error behavior, but given the simplicity and the presence of sibling tools, the description is reasonably 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?

The input schema already covers all three parameters with descriptions (100% coverage). The description adds only the inclusive range caveat and an example format via the schema. This is marginally beyond the schema but does not substantially enhance understanding of the parameters themselves.

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 historical electricity prices between two ISO 8601 dates, with the inclusive range nuance. It distinguishes from siblings like get_current_price and get_day_ahead_prices by explicitly mentioning 'historical' and gives concrete use cases (backtesting, charts, 'what did power cost last week?').

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 provides clear usage context: backtesting, charts, and historical queries. It also mentions a prerequisite (API plan requirement), which helps the agent determine feasibility. However, it does not explicitly name alternatives or state when not to use this tool, so it falls short of a 5.

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 full burden of behavioral disclosure. It clearly communicates the read-only nature (list) and expected output (zone codes with country names). While it doesn't mention edge cases like pagination or all zones being returned, the phrase 'all bidding-zone codes' covers scope comprehensively enough for a simple lookup 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?

Two short sentences, front-loaded with the primary purpose, followed by a directly usable usage case. Every word earns its place, with no filler or redundant phrasing.

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 simplicity (zero params, no output schema), the description covers the needed context: what the tool returns and when to use it. It doesn't detail return structure, but the description inherently implies a list of zone codes with country names. The guidance about ZONE_NOT_FOUND error adds situational completeness, though a tiny gap remains about whether the list is static or time-dependent.

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 no parameters, so the schema has 100% coverage vacuously. The description adds no parameter-specific detail, but with zero parameters, none is needed. Baseline 4 is appropriate because there are no parameter semantics to clarify.

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 uses a specific verb ('List') and clearly defines the resource ('all bidding-zone codes supported by GridPulse') and output ('with friendly country names'). It is distinct from sibling tools like get_current_price or get_carbon_intensity, which focus on data retrieval rather than zone enumeration.

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 states when to invoke the tool: 'when the user’s zone is ambiguous or after a ZONE_NOT_FOUND error.' This provides clear context and differentiates it from other tools that operate on a specific zone, making it easy for an agent to select the right tool.

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 for behavioral disclosure. It provides units and indicates 'current' conditions, but omits the forecast option and any error handling. This is minimal but not misleading.

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 two sentences, front-loaded with the core purpose and units, and includes only relevant use-case details. No unnecessary words.

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?

The description does not mention the forecast parameter, which is a notable omission given it is a primary feature. It also doesn't direct users to get_supported_zones for valid zones, though the schema covers this. Without an output schema, the description should be more comprehensive.

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 100% for both parameters, so the baseline is 3. The description adds no parameter-specific information beyond what the schema already provides.

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 retrieves weather data (wind speed, solar radiation, temperature) for a zone, using a specific verb and resource. It distinguishes itself from sibling tools focused on prices, carbon, and schedules.

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 gives explicit use cases: understanding electricity price fluctuations and correlating weather with generation mix. It does not mention alternative tools or when not to use it, but the context is clear enough.

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