teslamate-mcp

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 mentions the return content (charging statistics) but lacks details on permissions, rate limits, data freshness, or error handling. For a tool with no annotations, this is a significant gap in transparency, though it does specify what data is returned.

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 and front-loaded, consisting of two sentences that directly state the purpose and return value without any fluff. Every sentence earns its place by providing essential information efficiently, making it easy for an agent to parse quickly.

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 has 0 parameters, 100% schema coverage, and an output schema exists, the description is relatively complete. It explains what the tool does and what it returns, which is sufficient for a simple read operation. However, it could improve by adding more behavioral context, such as data scope or limitations, to fully compensate for the lack of annotations.

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 0 parameters with 100% coverage, so no parameter documentation is needed. The description doesn't add parameter details, which is appropriate, but it could have mentioned any implicit filters or defaults. Since there are no parameters, a baseline of 4 is applied, as the description doesn't need to compensate for 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 purpose: 'Get the summary of all charging sessions for each car' specifies the verb (get) and resource (charging sessions summary), and distinguishes it from siblings like get_charging_by_location or get_daily_battery_usage_patterns. However, it doesn't explicitly differentiate from all siblings, such as get_total_distance_and_efficiency, which might also involve summaries.

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 no guidance on when to use this tool versus alternatives. It doesn't mention any prerequisites, exclusions, or specific contexts, such as comparing it to get_charging_by_location for location-based data or get_daily_battery_usage_patterns for time-based insights. This leaves the agent without clear usage instructions.

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 describes a read operation ('Get') but doesn't mention any behavioral traits such as data freshness, rate limits, authentication needs, or what 'average efficiency' entails (e.g., units, calculation method). The description is too vague to fully inform an agent about how the tool behaves beyond its basic purpose.

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 concise sentences that are front-loaded with the core purpose. Every word earns its place: the first sentence states what the tool does, and the second provides context without redundancy. There's no wasted text or unnecessary elaboration.

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 (a read operation with no parameters) and the presence of an output schema, the description is minimally adequate. It states the purpose but lacks details on behavioral aspects like data sources or limitations. With no annotations and an output schema, the description should ideally provide more context about what 'average efficiency' means or how results are structured, but it's not entirely incomplete.

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 tool has 0 parameters, and schema description coverage is 100%. The description doesn't need to explain parameters, and it appropriately doesn't mention any. A baseline of 4 is applied since no parameters exist, and the description doesn't incorrectly suggest otherwise.

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 average efficiency by temperature for each car.' It specifies the verb ('Get'), resource ('average efficiency by temperature'), and scope ('for each car'). However, it doesn't explicitly differentiate from sibling tools like 'get_efficiency_by_month_and_temperature' or 'get_total_distance_and_efficiency', which prevents a perfect 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?

The description provides implied usage context: 'Helps understand how temperature affects vehicle efficiency.' This suggests when to use the tool (for temperature-efficiency analysis) but doesn't explicitly state when not to use it or name alternatives among the many sibling tools. No explicit guidance on prerequisites or comparisons is given.

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 fields (VIN, model, firmware version, and other vehicle details) but lacks behavioral context such as whether this is a read-only operation, if it requires authentication, rate limits, or how data is sourced (e.g., from a database vs. live API). For a tool with no annotations, this is a significant gap in transparency.

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 and front-loaded, stating the purpose and return values in two clear sentences without unnecessary details. However, it could be slightly more structured by explicitly separating purpose from output, but it's efficient and 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 has 0 parameters, 100% schema coverage, and an output schema exists (so return values needn't be explained), the description is minimally complete. It covers purpose and hints at output but lacks behavioral context (e.g., read-only nature, data freshness), which is a gap for a tool with no annotations. It's adequate but not fully 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?

The tool has 0 parameters, and schema description coverage is 100%, so no parameter documentation is needed. The description doesn't add param info, which is appropriate, but it could have noted the lack of inputs more explicitly. Baseline for 0 params is 4, as it adequately handles the absence of parameters.

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 basic car information for each car' specifies the verb ('Get') and resource ('basic car information'), and it distinguishes from siblings by focusing on static vehicle details rather than dynamic metrics like status, charging, or driving patterns. However, it doesn't explicitly contrast with all siblings (e.g., 'get_current_car_status' might overlap in some details), keeping it from a perfect 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?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, context (e.g., for initial setup vs. ongoing monitoring), or compare to siblings like 'get_current_car_status' for real-time data, leaving the agent to infer usage based on purpose alone.

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 mentions tracking battery health metrics and capacity changes, which implies a read-only operation, but doesn't specify data freshness, time range defaults, aggregation methods, or output format. This leaves significant behavioral gaps for a tool that likely returns time-series data.

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 appropriately concise with two clear sentences that directly address the tool's purpose. It avoids unnecessary elaboration while maintaining readability, though it could be slightly more front-loaded by combining concepts more efficiently.

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 (tracking degradation over time), the presence of an output schema helps, but the description lacks sufficient context about what 'over time' means (e.g., daily, monthly, all-time), how data is aggregated, or what specific metrics are included. With no annotations and minimal behavioral details, it's adequate but has clear 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 tool has zero parameters with 100% schema description coverage, so the schema fully documents the input requirements. The description doesn't need to explain parameters, and it appropriately avoids redundant information, earning a baseline score of 4 for parameter-free tools.

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 with specific verbs ('get', 'tracks') and resources ('battery degradation over time', 'battery health metrics', 'capacity changes'). It distinguishes from some siblings like 'get_battery_health_summary' by focusing on temporal trends rather than summary statistics, though it doesn't explicitly differentiate from all potential alternatives.

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 no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, timing considerations, or compare it to siblings like 'get_battery_health_summary' or 'get_daily_battery_usage_patterns', leaving the agent to infer usage context independently.

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 for behavioral disclosure. It mentions the tool 'provides current battery health status and statistics' which suggests a read-only operation, but doesn't specify format, scope (all cars vs filtered), response structure, or any limitations. For a tool with zero annotation coverage, this leaves significant behavioral questions unanswered.

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 perfectly concise with two sentences that each add value. The first sentence states the core purpose, the second adds important detail about what's provided. No wasted words, no redundancy, and front-loaded with the 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?

Given the tool has 0 parameters, 100% schema coverage, and an output schema exists, the description is reasonably complete for its purpose. However, with no annotations and multiple sibling tools in the same domain, it should ideally provide more context about when this summary tool is appropriate versus other battery-related tools. The existence of an output schema means the description doesn't need to explain return values.

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 tool has 0 parameters with 100% schema description coverage, so the schema already fully documents the parameter situation. The description appropriately doesn't discuss parameters since none exist. Baseline for 0 parameters with full schema coverage is 4, as the description correctly avoids redundant parameter information.

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 action ('Get') and resource ('battery health summary for each car'), specifying it provides status and statistics. It distinguishes from siblings like 'get_battery_degradation_over_time' by focusing on current status rather than historical trends. However, it doesn't explicitly differentiate from all battery-related tools like 'get_daily_battery_usage_patterns'.

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 no guidance on when to use this tool versus alternatives. With multiple battery-related siblings (get_battery_degradation_over_time, get_daily_battery_usage_patterns), there's no indication of when this summary tool is preferred over more specific battery tools. No prerequisites or exclusions are mentioned.

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 for behavioral disclosure. It states what the tool returns ('charging patterns and statistics grouped by location') but doesn't disclose whether this is a read-only operation, whether it requires authentication, rate limits, data freshness, or error conditions. For a tool with zero annotation coverage, this leaves significant behavioral 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 appropriately concise with two sentences that each add value. The first sentence states the core purpose, and the second adds context about what's shown. There's no wasted verbiage or repetition. However, it could be slightly more front-loaded by combining the two ideas more tightly.

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 that the tool has no parameters, has an output schema (which handles return values), and relatively simple functionality, the description is reasonably complete. It explains what data is retrieved and how it's organized. However, for a tool with no annotations, it should ideally provide more behavioral context about the operation's characteristics and limitations.

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 tool has zero parameters with 100% schema description coverage, so the baseline is 4. The description doesn't need to explain parameters since none exist, and it appropriately focuses on what the tool returns rather than parameter semantics.

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 charging by location for each car' specifies the verb (get) and resource (charging by location). It distinguishes from siblings by focusing on location-based charging patterns rather than summaries, efficiency, or other metrics. However, it doesn't explicitly differentiate from 'get_most_visited_locations' which might overlap conceptually.

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 no guidance on when to use this tool versus alternatives. It mentions 'Shows charging patterns and statistics grouped by location' but doesn't indicate when this is preferable to other charging-related tools like 'get_all_charging_sessions_summary' or location-related tools like 'get_most_visited_locations'. No explicit when/when-not statements or alternative recommendations are 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?

No annotations are provided, so the description carries full burden. It mentions 'real-time vehicle status' and lists data types (location, battery level, state), but doesn't disclose behavioral traits like rate limits, authentication needs, data freshness, or whether it returns all cars or requires filtering. For a tool with zero annotation coverage, this is a significant gap in transparency.

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 appropriately sized and front-loaded: the first sentence states the core purpose, and the second adds key details about return values. Both sentences earn their place by clarifying scope and data types. It could be slightly more structured by explicitly separating purpose from output details.

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 has 0 parameters, 100% schema coverage, and an output schema exists, the description is reasonably complete. It explains what the tool does and hints at return values, though it could better address behavioral aspects like real-time constraints. The output schema likely covers return details, reducing the need for extensive 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?

The tool has 0 parameters, and schema description coverage is 100%, so no parameter documentation is needed. The description doesn't add parameter semantics beyond the schema, but with no parameters, a baseline score of 4 is appropriate as there's nothing to compensate for.

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 current car status for each car' specifies the verb (get) and resource (car status). It distinguishes from siblings by focusing on real-time status rather than historical summaries or specific metrics like battery degradation or charging sessions. However, it doesn't explicitly contrast with 'get_basic_car_information', which might overlap.

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 no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, timing considerations (e.g., real-time vs. cached data), or compare to siblings like 'get_basic_car_information' or 'get_total_distance_and_efficiency'. Usage is implied by the real-time focus but not explicitly stated.

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 states the tool 'Analyzes battery consumption patterns,' implying a read-only operation, but doesn't cover critical aspects like data freshness, rate limits, authentication needs, or output format. This leaves significant gaps in understanding how the tool behaves in practice.

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 front-loaded and efficient, using two concise sentences that directly state the tool's purpose and analysis scope without any redundant or unnecessary information. Every sentence earns its place by contributing to understanding the tool's function.

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 (implied analysis of patterns), no annotations, and an output schema present, the description is minimally adequate. It covers the basic purpose but lacks details on behavioral traits, usage context, or what the output entails, relying on the output schema to fill in return values. This leaves room for improvement in 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?

The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description adds no parameter information, which is appropriate here, but it doesn't compensate for any gaps since there are none. A baseline of 4 is applied as it meets the requirement for a tool with no parameters.

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 with a specific verb ('Get') and resource ('daily battery usage patterns for each car'), and distinguishes it from siblings like 'get_daily_driving_patterns' by focusing on battery consumption rather than driving. However, it doesn't explicitly differentiate from all siblings, such as 'get_battery_health_summary' or 'get_unusual_power_consumption', which might overlap in battery-related analysis.

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 no guidance on when to use this tool versus alternatives. It mentions analyzing 'battery consumption patterns throughout the day' but doesn't specify contexts, prerequisites, or exclusions, leaving the agent to infer usage from the purpose alone without explicit direction.

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 states the tool 'Get[s]' data, implying a read-only operation, but doesn't clarify aspects like authentication needs, rate limits, data freshness, or whether it aggregates historical data. The description adds minimal context beyond the basic purpose, missing key behavioral traits for a tool with no annotation support.

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 concise sentences that efficiently convey the tool's purpose and output focus. The first sentence states the action and resource, and the second elaborates on the data's nature. There is no wasted language, and it is front-loaded with the core functionality.

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 has 0 parameters, 100% schema coverage, and an output schema, the description is minimally adequate. It explains what the tool does and the type of data returned, but with no annotations and many sibling tools, it lacks guidance on usage and behavioral details. The output schema likely covers return values, so the description's focus on semantics is sufficient, but overall completeness is limited by the missing context for selection and 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 has 0 parameters with 100% description coverage, so no parameter documentation is needed. The description doesn't discuss parameters, which is appropriate given the schema's completeness. It adds value by explaining the output's focus on 'driving habits and patterns by day of week and time', which helps the agent understand the return data's structure beyond what the output schema might provide.

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 daily driving patterns for each car' specifies the verb (get) and resource (daily driving patterns for each car). It distinguishes from siblings by focusing on driving habits by day/time, unlike tools for charging, efficiency, or battery health. However, it doesn't explicitly differentiate from 'get_daily_battery_usage_patterns' or 'get_drive_summary_per_day', which might overlap in scope.

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 no guidance on when to use this tool versus alternatives. It mentions 'driving habits and patterns by day of week and time' but doesn't specify use cases, prerequisites, or exclusions. With many sibling tools (e.g., 'get_daily_battery_usage_patterns', 'get_drive_summary_per_day'), the lack of explicit comparison leaves the agent guessing about the best choice.

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 for behavioral disclosure. It states what data is returned but doesn't cover critical aspects like whether this is a read-only operation, authentication requirements, rate limits, or how data is formatted (e.g., aggregated vs. raw). This is inadequate for a tool with no annotation coverage.

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 and front-loaded: two sentences that directly state the tool's purpose and what it provides, with no wasted words. Every sentence adds value by clarifying the scope and content of the summary.

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 has no parameters, an output schema exists, and no annotations are provided, the description is minimally adequate. It explains what data is returned but lacks context on behavioral traits like safety or performance. With output schema handling return values, the description meets basic needs but could improve by addressing usage scenarios.

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 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately doesn't discuss parameters, focusing instead on the tool's output semantics, which aligns with the baseline for zero-parameter tools.

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 drive summary per day for each car' with specific details about what statistics are included (distance, duration, efficiency). It distinguishes itself from siblings like 'get_daily_driving_patterns' by focusing on summary statistics rather than patterns, though the differentiation could be more explicit.

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 no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, timing considerations, or how it differs from similar tools like 'get_daily_driving_patterns' or 'get_monthly_driving_summary,' leaving the agent without context for 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 are provided, so the description carries the full burden of behavioral disclosure. It mentions analysis but doesn't specify whether this is a read-only operation, if it requires authentication, what data format is returned, or any performance characteristics. For a tool with zero annotation coverage, this leaves significant behavioral 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 efficiently structured in two sentences: the first states the core functionality, and the second adds analytical context. Every sentence earns its place without redundancy or unnecessary elaboration, making it appropriately front-loaded and 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?

Given the tool's complexity (analysis of efficiency by month and temperature) and the presence of an output schema, the description is minimally adequate. It explains what the tool does but lacks details on behavioral traits, usage context, or output interpretation. With no annotations and rich sibling tools, it should provide more guidance to be 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?

The tool has 0 parameters with 100% schema description coverage, so the schema fully documents the absence of inputs. The description doesn't need to compensate for any parameter gaps, and it appropriately doesn't mention parameters. A baseline of 4 is applied since no parameters exist.

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 with a specific verb ('Get') and resource ('efficiency by month and temperature for each car'), and adds analytical context ('Analyzes how seasonal temperature changes affect vehicle efficiency'). However, it doesn't explicitly differentiate from siblings like 'get_average_efficiency_by_temperature' or 'get_total_distance_and_efficiency', which prevents a perfect 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?

The description provides no guidance on when to use this tool versus alternatives. With multiple sibling tools related to efficiency and temperature analysis, there's no indication of specific use cases, prerequisites, or comparisons to tools like 'get_average_efficiency_by_temperature' or 'get_monthly_driving_summary'.

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 for behavioral disclosure. It states the tool 'lists' trips with details, implying a read-only operation, but doesn't cover critical aspects like data freshness, pagination, error conditions, or performance characteristics. This is inadequate for a tool with zero annotation coverage.

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 efficiently structured in two sentences: the first states the core purpose, and the second adds clarifying details about output content. Every sentence adds value without redundancy, making it appropriately concise 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 the tool has an output schema (which handles return values) and no parameters, the description's coverage of purpose and output details is minimally adequate. However, with no annotations and behavioral gaps, it doesn't fully address the tool's operational context, leaving room for improvement in guiding the agent.

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 0 parameters with 100% coverage, so no parameter documentation is needed. The description appropriately doesn't discuss parameters, earning a baseline score of 4 for not adding unnecessary 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 the tool's purpose: 'Get the longest drives by distance for each car' specifies the verb (get) and resource (longest drives by distance), with additional context about listing trips with distance and duration details. It distinguishes from siblings by focusing on longest trips rather than summaries, averages, or other metrics, though it doesn't explicitly name alternatives.

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 no guidance on when to use this tool versus alternatives. It mentions 'for each car' but doesn't specify prerequisites, context, or exclusions compared to sibling tools like get_drive_summary_per_day or get_total_distance_and_efficiency, leaving the agent to infer usage based on purpose alone.

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 states what data is provided (monthly statistics) but does not cover aspects like read-only nature, performance, error handling, or data freshness. For a tool with no annotations, this leaves significant gaps in understanding its 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?

The description is concise and well-structured with two sentences: the first states the purpose, and the second elaborates on the statistics provided. Every sentence adds value without redundancy, making it front-loaded and efficient.

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 has no parameters, an output schema exists, and no annotations, the description is minimally adequate. It explains what the tool returns (monthly statistics for distance, energy usage, costs) but lacks details on output structure or behavioral context. With an output schema, the description need not explain return values in depth, but it could benefit from more context on usage or limitations.

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 tool has 0 parameters, and schema description coverage is 100%, so there are no parameters to document. The description does not need to add parameter semantics beyond the schema, and it appropriately focuses on the tool's output. A baseline score of 4 is applied as it handles the lack of parameters effectively.

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 monthly driving summary for each car' specifies the verb (get) and resource (monthly driving summary), and 'Provides monthly statistics for distance, energy usage, and costs' elaborates on the content. However, it does not explicitly differentiate from siblings like 'get_daily_driving_patterns' or 'get_total_distance_and_efficiency', which reduces clarity.

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 no guidance on when to use this tool versus alternatives. It does not mention prerequisites, context, or exclusions, nor does it reference sibling tools for comparison. Usage is implied by the purpose but lacks explicit direction.

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 for behavioral disclosure. It mentions output content ('frequently visited places with visit counts and durations') but doesn't cover critical aspects like whether this requires authentication, rate limits, data freshness, or how 'most visited' is calculated (e.g., time range, threshold). For a tool with no annotation coverage, this leaves significant 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 concise sentences that efficiently convey the core functionality and output details. Every word earns its place: the first sentence states the purpose, and the second elaborates on the output format. No wasted text 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?

Given zero parameters, 100% schema coverage, and an output schema exists, the description doesn't need to explain parameters or return values. However, as a data retrieval tool with no annotations, it should ideally mention behavioral aspects like authentication needs or data scope (e.g., time range). The description is minimally adequate but lacks context for safe, effective use.

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 tool has zero parameters, and schema description coverage is 100%, so no parameter documentation is needed. The description appropriately doesn't discuss parameters, focusing instead on what the tool returns. A baseline of 4 is applied for zero-parameter tools when the schema is fully descriptive.

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 most visited locations for each car' specifies the verb (get) and resource (most visited locations per car). It distinguishes from siblings by focusing on location visitation patterns rather than charging, efficiency, or other metrics. However, it doesn't explicitly differentiate from potential location-related siblings like 'get_charging_by_location'.

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 no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, timing considerations, or compare to sibling tools that might overlap in scope (like 'get_charging_by_location' for location data). The agent must infer usage from the purpose alone.

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 states the tool retrieves historical data, implying a read-only operation, but does not clarify aspects like authentication needs, rate limits, data freshness, or error handling. This is a significant gap for a tool with no annotation coverage.

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 appropriately sized and front-loaded, consisting of two concise sentences that directly state the tool's purpose and scope without any redundant information. Every sentence earns its place by adding 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?

Given the tool's complexity (simple data retrieval with no parameters) and the presence of an output schema (which handles return values), the description is minimally adequate. However, it lacks behavioral details (e.g., permissions, limitations) that would be helpful since no annotations are provided, making it incomplete for optimal agent use.

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 0 parameters with 100% coverage, so no parameter documentation is needed. The description adds value by explaining what data is retrieved ('software update history for each car' and 'tracks firmware updates and version changes over time'), which provides context beyond the empty 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 purpose with a specific verb ('Get') and resource ('software update history for each car'), and specifies what it tracks ('firmware updates and version changes over time'). However, it does not explicitly differentiate from sibling tools, which are all data retrieval tools but focus on different car metrics like charging, efficiency, or driving patterns.

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 no guidance on when to use this tool versus alternatives. It mentions what the tool does but does not specify contexts, prerequisites, or exclusions, leaving the agent to infer usage based on the tool name alone among many sibling data-fetching 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 of behavioral disclosure. It states the tool 'Monitors tire pressure changes and patterns by week,' which implies a read-only, monitoring function, but doesn't specify details like data format, time ranges, error handling, or authentication needs. For a tool with zero annotation coverage, this leaves significant behavioral gaps, such as whether it requires specific permissions or how it handles missing data.

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 and front-loaded: the first sentence states the core purpose, and the second adds context. Both sentences earn their place by clarifying the tool's function and monitoring aspect. It avoids redundancy and is appropriately sized for a no-parameter tool, though it could be slightly more structured with bullet points or examples.

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 (simple, no parameters) and the presence of an output schema, the description is somewhat complete but has gaps. It explains what the tool does but lacks behavioral details like data scope or usage context. The output schema likely covers return values, reducing the need for that in the description, but without annotations, more context on monitoring behavior would improve 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?

The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description doesn't add param info, which is appropriate here. Since there are no parameters, the baseline score is 4, as the description doesn't need to compensate for any schema gaps and aligns with the schema's simplicity.

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 tire pressure weekly trends for each car.' It specifies the verb ('Get'), resource ('tire pressure weekly trends'), and scope ('for each car'), which is specific and actionable. However, it doesn't explicitly distinguish this tool from potential siblings like 'get_current_car_status' or 'get_daily_driving_patterns', which might also involve tire pressure or car monitoring, so it doesn't fully achieve sibling differentiation.

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 no guidance on when to use this tool versus alternatives. It mentions 'Monitors tire pressure changes and patterns by week,' which implies a monitoring context, but doesn't specify use cases, prerequisites, or exclusions. Without explicit when/when-not instructions or named alternatives, the agent lacks clear usage direction.

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 for behavioral disclosure. It states what the tool does ('Get... statistics') but lacks critical behavioral details: it doesn't specify if this is a read-only operation, whether it requires authentication, how data is returned (e.g., format, pagination), or any rate limits. For a tool with zero annotation coverage, this is a significant gap in transparency.

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 appropriately concise with two sentences that directly state the tool's function and scope ('lifetime statistics'). It's front-loaded with the core purpose, and every sentence adds value without redundancy. Minor improvement could be made by integrating the two sentences more tightly, but it's efficient overall.

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 low complexity (0 parameters) and the presence of an output schema (which handles return values), the description is minimally adequate. However, with no annotations and siblings offering similar data, it lacks completeness in behavioral context (e.g., safety, performance) and usage differentiation. It meets basic needs but leaves gaps for an agent to operate effectively in this crowded toolset.

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 tool has 0 parameters with 100% schema description coverage, so the schema fully documents the absence of inputs. The description adds no parameter information, which is appropriate here. Baseline is 4 for 0 parameters, as no compensation is needed, and the description doesn't introduce confusion about inputs.

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 total distance and efficiency for each car' with the verb 'Get' and resources 'distance and efficiency for each car'. It distinguishes from siblings by specifying 'lifetime statistics', unlike tools focused on daily/monthly patterns or specific conditions. However, it doesn't explicitly differentiate from all siblings (e.g., 'get_basic_car_information' might overlap).

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 no guidance on when to use this tool versus alternatives. It mentions 'lifetime statistics', which implies a broad historical scope, but doesn't specify use cases, prerequisites, or exclusions compared to siblings like 'get_monthly_driving_summary' or 'get_efficiency_by_month_and_temperature'. Without explicit when/when-not instructions, the agent must infer usage from context alone.

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 states the tool 'Get[s]' data and identifies anomalies, implying a read-only operation, but doesn't clarify aspects like whether it requires authentication, how anomalies are defined (e.g., thresholds, algorithms), rate limits, or what 'unusual' means contextually. For a tool with zero annotation coverage, this leaves significant gaps in understanding its 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?

The description is highly concise and front-loaded, consisting of two clear sentences that directly state the tool's function and added value (anomaly identification). Every word earns its place without redundancy or fluff, making it easy for an agent to parse quickly.

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 has 0 parameters, 100% schema coverage, and an output schema exists (so return values are documented elsewhere), the description is minimally adequate. It explains what the tool does but lacks details on behavioral traits (e.g., how anomalies are detected) and usage context. For a diagnostic tool with no annotations, it should provide more guidance on interpretation or limitations to be 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?

The input schema has 0 parameters with 100% coverage, so there's no need for parameter documentation in the description. The description appropriately avoids discussing parameters, focusing instead on the tool's purpose. A baseline of 4 is applied as it doesn't add unnecessary param info, but it doesn't reach 5 since it doesn't enhance schema understanding (which is already complete).

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 with a specific verb ('Get') and resource ('unusual power consumption for each car'), and adds context about identifying anomalies. It distinguishes itself from siblings by focusing on power consumption anomalies rather than summaries, averages, or other metrics. However, it doesn't explicitly differentiate from all possible siblings (e.g., 'get_battery_health_summary' might overlap in detecting issues).

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 no guidance on when to use this tool versus alternatives. It mentions identifying anomalies 'that might indicate issues,' which implies a diagnostic context, but doesn't specify prerequisites, timing, or compare it to other tools like 'get_battery_health_summary' or 'get_current_car_status' that might also flag problems. Without explicit when-to-use or when-not-to-use instructions, the agent must infer usage.

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