GridPulse Energy

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

No annotations provided, so description is the sole source. It indicates ranked output and labels but doesn't disclose error handling (e.g., invalid zones) or data source behavior. Adequate but 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?

Two sentences that efficiently convey purpose, usage context, and labeling. No redundancy, front-loaded with main action.

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 explains return is ranked with intensity labels. Sufficient for a simple comparison tool; could mention ordering direction explicitly.

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 already describes the zones parameter well (format, max 10). Description adds no extra semantic detail beyond the tool's purpose, 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 compares carbon intensity across multiple zones, ranked cleanest first, using specific metric gCO2/kWh. It distinguishes from siblings like get_carbon_intensity (single zone) and compare_green (likely different 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?

It explains when to use (user asks for cleanest grid or lowest-carbon location) and provides intensity range labels. It implicitly differentiates from single-zone tools but doesn't explicitly mention when not to use.

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 of behavioral disclosure. It details the scoring formula (renewables percentage at 60% weight, carbon intensity at 40% weight), label ranges (excellent, good, etc.), and that results are ranked greenest first. This adds significant behavioral context beyond what is available from structured fields.

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 with no wasted words. It contains three sentences that efficiently convey purpose, scoring, label ranges, and usage context. 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?

The description explains the scoring algorithm, label ranges, and ranking. However, it lacks information about the output format (e.g., returns a list of zones with scores and labels) and does not mention any potential limitations or error conditions. Given there is no output schema, the description could be slightly more 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?

Input schema has 100% description coverage with a single parameter 'zones' described as 'Comma-separated bidding zone codes, e.g. 'DE,FR,NO,GB,CAISO'. Max 10 zones.' The description does not add parameter details beyond the schema, so 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 clearly states the tool's purpose: 'Compare green scores (0-100) across multiple zones in one call, ranked greenest first.' It uses a specific verb ('compare') and resource ('green scores'), and distinguishes from sibling tools like compare_carbon and compare_prices by focusing on green scores.

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 says 'Use when the user wants to find the greenest grid for scheduling workloads.' This provides clear context for when to use the tool, though it does not explicitly mention when not to use or alternative 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 provided, so description carries full burden. Explains currency-based ranking and grouping, but omits response format, error handling, data freshness, and whether operation is read-only. Adequate but not comprehensive.

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?

Three sentences that front-load purpose and output format, include usage scenarios, and add currency detail. No fluff; 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?

Context signals show low complexity (2 params, 100% coverage, no nested objects). Description covers purpose, usage, and currency, but lacks output structure details (e.g., list of zone objects with price and rank) which would help agent without output schema.

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 covers 100% of parameters with descriptions. Description adds value beyond schema by explaining ranking logic per currency and the 'rank null' behavior for mixed-currency zones, clarifying the currency parameter's effect.

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?

Description clearly states verb 'compare', resource 'electricity spot prices', and output 'ranked list cheapest first'. Distinguishes from siblings like compare_carbon and get_current_price via explicit scope 'multiple zones' and ranking output.

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 cases ('which country has the cheapest electricity right now?') and constraints (max 10 zones, currency grouping). Does not explicitly exclude single-zone queries or mention alternatives, but sibling context fills gap.

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 must carry the full burden of behavioral disclosure. It states the output is 'current' and in gCO2/kWh, but does not disclose update frequency, data source, rate limits, or any potential caching behavior. This lack of detail could lead to incorrect assumptions about data freshness.

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 extremely concise with two sentences: the first states the core function, and the second adds interpretation and use cases. Every sentence earns its place with no fluff or redundancy.

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 tool with one parameter and no output schema, the description provides adequate context: what it does, the unit, and typical use cases. Minor gaps exist, such as data source or refresh interval, but overall it is fairly complete for an agent to understand and use 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 coverage is 100% with a clear description of the 'zone' parameter. The tool description adds minimal extra meaning beyond the schema (e.g., 'Bidding zone code'). Since the schema already explains the parameter, the description does not provide significant added value, warranting a baseline score 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 that the tool retrieves current grid electricity carbon intensity for a zone in gCO2/kWh. It uses a specific verb ('get') and resource ('carbon intensity'). However, it does not explicitly differentiate from siblings like compare_carbon or get_global_carbon, which could cause confusion.

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 suggests using the tool for carbon-aware workload routing or comparing cleanliness across times or regions, providing clear context. However, it does not mention alternatives or when not to use it, which would help the agent choose among sibling 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 explains the internal process (calls API cheapest endpoint, refines using day-ahead prices) and clarifies deadline interpretation (UTC, rollover to next day if past). This gives good insight into expected behavior, though it omits details like rate limits or error handling.

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 superfluous words. The first sentence front-loads the purpose, and the second provides necessary technical detail. 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?

Given 3 parameters and no output schema, the description covers the core algorithm and deadline handling. It is fairly complete but lacks mention of the output format or potential errors, which would be helpful for a tool with no output schema.

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%, so baseline is 3. The description adds value by explaining the deadline parameter's interpretation (UTC, rollover to next day), which is not fully captured in the schema. This enhances understanding beyond the basic 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 finds the cheapest contiguous hours for flexible loads before a deadline, using a specific verb and resource. It distinguishes from siblings like 'get_optimal_schedule' by emphasizing the contiguous and deadline aspects.

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 to use the tool (scheduling flexible loads before a deadline) but does not explicitly mention alternatives or when not to use it. The context is sufficient for an agent to select it appropriately among energy scheduling 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 provided. Description discloses it returns latest price for one zone, optionally with carbon/renewables, but lacks details on data freshness, caching, or operational constraints.

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, no wasted words. Front-loaded with core function, followed by usage and optional features. Efficient and 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?

Covers purpose, usage, and parameter details adequately for a simple tool. Lacks return format description, but given no output schema and tool simplicity, it is fairly 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%, but description adds meaning with examples for zone (DE, FR) and explains include parameter values (carbon, generation). Enhances understanding beyond 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 gets the latest available electricity spot price for one bidding zone, specifying verb, resource, and unit (EUR/MWh). It distinguishes from siblings by focusing on 'current price' as opposed to day-ahead or history.

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 asks what power costs right now, or needs a fresh price snapshot.' Provides clear when-to-use guidance, but does not mention exclusions or alternatives.

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 full burden. It mentions the return format (time series in EUR/MWh) and time horizon (tomorrow's and near-future), but lacks details on authentication, rate limits, or potential errors. This is adequate for a simple read operation but leaves 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?

Two sentences with no wasted words. First sentence states purpose and scope, second provides use cases and return format. Highly efficient and front-loaded.

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 one parameter with full coverage and no output schema, the description covers core functionality well. It explains what is returned and typical uses, but could be enhanced by describing the output structure more precisely (e.g., fields in the time series).

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% (only parameter 'zone' has a description pointing to get_supported_zones). The description adds no additional parameter details beyond what the schema provides, meeting the baseline but not exceeding 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 it gets 'tomorrow's hourly day-ahead electricity prices for a zone', provides use cases (show daily price curve, find low-price hours, debug scheduling), and implicitly differentiates from siblings like get_current_price and get_price_history by specifying 'day-ahead'.

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 context on when to use the tool (e.g., 'show the full daily price curve, find low-price hours visually, or debug scheduling'), but does not explicitly state when to avoid it or mention alternative tools for related tasks.

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 bears full burden. It discloses that data is 'Updated daily' and describes the return fields. However, it does not mention potential errors, rate limits, or authentication needs. For a simple read-only tool, this is reasonable.

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 key outputs, followed by relevance explanation. Every sentence is necessary and no superfluous text.

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 simple input, the description adequately explains what is returned (percentage, trend, average, risk signal). It does not specify exact JSON structure but provides enough detail for decision-making.

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%, but the description adds value by providing examples (ISO2 country codes like DE, FR) and constraints (EU country with gas storage data). This helps the agent select valid input.

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 current natural gas storage levels for EU countries, with specific outputs (percentage, trend, 5-year average comparison, price risk signal). It is distinct from sibling tools focused on electricity prices or carbon 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 implies use when gas storage data is needed to analyze electricity price influences. While it doesn't explicitly state when not to use or compare alternatives, the context is clear given sibling tools like get_current_price.

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 bears the full burden of behavioral disclosure. It clearly states the output (prices, spread, system state) and source (Elexon balancing mechanism). However, it could be enhanced by mentioning update frequency, data reliability, or any prerequisites, which are absent.

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, no wasted words. The first sentence covers purpose and output; the second covers target audience and use case. Every sentence earns its place, and the structure is front-loaded.

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 no parameters and no output schema, the description fully explains what the tool returns (day-ahead price, real-time prices, spread, system state) and who should use it. No gaps remain given the tool's simplicity.

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, and schema description coverage is 100% (trivially). The description adds no parameter information, which is acceptable since none exist. Baseline for 0 params is 4, and the tool meets that standard.

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 the verb 'Get', resource 'Great Britain electricity market summary', and details the exact data shown (day-ahead vs real-time prices, spread, system long/short). It clearly distinguishes from sibling tools like get_current_price or get_day_ahead_prices by focusing on the GB market summary with balancing mechanism data.

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 it is 'Essential for GB battery storage operators and flexible demand managers', providing clear target users and context. However, it does not explicitly mention when not to use or list alternative tools from the sibling list, so it stops short of perfect guidance.

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, so description carries full burden. It describes the output (monthly carbon intensity) but does not disclose behavioral traits like read-only nature, rate limits, or potential errors. As a read query, this is minimally acceptable.

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?

Three concise sentences: core function, examples, and use case. No redundancy or unnecessary fluff.

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 purpose, input, and use cases. It could mention output format or structure, but overall sufficient for selection and 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?

Schema coverage is 100% with parameter description already including ISO2 examples. Description adds context (e.g., '70+ countries') but does not significantly enhance parameter understanding 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?

Clear verb+resource ('Get monthly carbon intensity') with specific unit (gCO2/kWh) and scope (global, ISO2 codes). However, it does not explicitly differentiate from sibling tools like 'get_carbon_intensity' or 'compare_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?

Provides application context ('Use for Scope 2 emissions calculations or comparing carbon intensity') but lacks explicit when-not-to-use or alternative comparisons.

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 does not disclose data freshness (real-time vs forecast), side effects, authentication requirements, or rate limits. The description explains the score's meaning but omits behavioral traits critical for an agent to invoke the tool safely and effectively.

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 concise sentences that front-load the core information (score range and meaning) followed by a use-case suggestion. No extraneous words, each 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?

Given the tool's simplicity (single parameter, no output schema), the description covers purpose, score interpretation, and usage guidance. It lacks behavioral transparency details (e.g., data source, update frequency), but for a basic score retrieval tool, the information is mostly 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 coverage is 100% with the zone parameter documented. The description adds no additional semantic meaning beyond the schema's description (bidding zone code). Baseline 3 is appropriate as the schema already fully defines the parameter.

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 returns a single 0-100 'green score' for a zone, with explicit mapping of high and low values. It also suggests a concrete use case ('should I run compute now?') and distinguishes from siblings like get_carbon_intensity and compare_green.

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 advises using the tool 'when a numeric score is easier than raw carbon + mix', providing clear context for when to prefer this over more detailed alternatives. It does not explicitly name sibling tools but gives enough guidance for appropriate selection.

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, so description carries full burden. Describes input (zones, hours, deadline) and output (ranked list using day-ahead prices and green scores). Does not disclose rate limits, data freshness, or side effects (likely none), but covers key behavioral aspects.

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, first conveying core purpose, second adding detail on ranking and optimization. Efficient and front-loaded, though slightly more structure could help scanning.

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 4 parameters, no output schema, and no annotations, the description covers purpose, inputs, optimization criteria. However, it doesn't describe the structure of the ranked list (e.g., zone, start hour) or error conditions, 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?

Schema coverage is 100% with parameter descriptions. Description adds context about optimization goals and deadline usage but does not significantly augment what is already in the schema. 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?

Description clearly states verb 'find the best contiguous hour block' and resource 'flexible compute or charging' across zones. Distinguishes from siblings like get_cheapest_window and get_green_score by specifying multi-objective 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?

Description sets context ('where flexible compute or charging could run') and explains optimization options. Lacks explicit when-not-to-use or alternative suggestions, but the usage scenario is clear.

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 disclose all behavioral traits. It mentions inclusive date range and ISO 8601 format but omits crucial details like whether the operation is read-only, rate limits, or any side effects. The note on API plan requirement adds some context, but overall transparency is insufficient.

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 highly concise, consisting of two sentences that front-load the core purpose and usage context. Every sentence adds value, and there is no redundant or extraneous 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?

The description effectively explains the tool's purpose, usage context, and a key prerequisite (API plan). While it does not describe the return format or handle edge cases, given the simple schema (3 simple string params, no output schema), the description is sufficiently complete for an AI agent to understand its operation.

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 covers all three parameters with descriptions. The description adds the concept of 'inclusive range' which aligns with schema, but does not provide additional semantic meaning beyond what is already in the schema. With 100% schema description coverage, a baseline 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 explicitly states the tool retrieves historical electricity prices, specifies the resource (electricity prices) and action (historical retrieval), and distinguishes from sibling tools like get_current_price (current) and get_day_ahead_prices (future) by focusing on past data.

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 use cases (backtesting, charts, querying past costs) and mentions a prerequisite (API plan requirement). However, it does not explicitly state when not to use this tool or offer direct comparisons to alternatives, leaving some ambiguity for selection among siblings.

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 states the tool lists zones and country names, which is appropriate for a read-only query. However, it does not mention any potential side effects or limitations, but none are expected.

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 followed by a usage hint. It is concise and front-loaded, with 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?

For a zero-parameter tool with no output schema, the description is fairly complete: it explains what is returned (zone codes and country names) and when to use it. It lacks details about the output format or any sorting, but the tool is simple enough.

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 has no parameters, and schema description coverage is 100%. The description does not need to add parameter information since there are none. It implicitly conveys that no parameters are needed.

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 lists all supported bidding-zone codes with friendly country names. It uses specific verbs and resource, and it is distinct from sibling tools which focus on prices, carbon, etc.

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 says when to call this tool: when the user's zone is ambiguous or after a ZONE_NOT_FOUND error. It provides clear context for usage, though it does not mention when not to use or suggest alternatives.

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 mentions 'current' and '48-hour forecast' but lacks details on data freshness, update frequency, accuracy, or any rate limits. The behavioral context is minimal.

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 functionality in the first sentence. Every word serves a purpose, with no redundancy.

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 should ideally describe the return structure or fields beyond the listed metrics. It mentions what data is returned but not the format (e.g., object, array). The tool is simple, so it 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 coverage is 100%. The description adds value by providing examples (DE, GB, NO, CAISO) and a reference to get_supported_zones for the zone parameter, and explains the forecast parameter's time range. This enhances the schema's meaning.

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 wind speed, solar radiation, and temperature for a zone, with specific units. It also explains the use-case connection to electricity prices, distinguishing it from other 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 explains when to use the tool (for understanding price drivers, correlating weather with generation mix). It does not explicitly mention when not to use or alternatives, but no weather-related sibling tools exist, making guidance sufficient.

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