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

No annotations are provided, so the description carries the burden. It usefully discloses the computation model (same-distance ICEV fuel vs. actual electricity cost) but says nothing about read-only nature, data source, permissions, or that it is a derived estimate rather than recorded data. Adequate but with clear gaps 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?

Two short sentences, front-loaded with the verb and resource, and the second sentence clarifies the comparison model. No filler, though the parenthetical Chinese gloss adds little for an English-language agent.

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?

An output schema exists and schema coverage is full, so return values and parameter formats need not be explained. What remains missing is usage context (when this beats tesla_savings) and the nature/source of the calculation, leaving the definition adequate but not 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%, so the schema already documents all five parameters (days, car_id, icev_mpg, gas_price, electricity_price) with defaults and units. The description adds the conceptual framing of the ICEV comparison but no syntax or semantics beyond the schema, so baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific computation: eco savings and CO2 reduction comparing Tesla electricity costs against a hypothetical ICEV, including tree equivalents. An agent can tell it apart from generic siblings like tesla_savings or tesla_trip_cost, though it never explicitly names which sibling to prefer.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No when-to-use guidance, no exclusions, and no routing against the nearby tesla_savings / tesla_trip_cost / tesla_efficiency tools that overlap conceptually. The agent must infer the situation from siblings 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, so the description carries the burden. It helpfully discloses the deterministic hash-of-date selection mechanism and the status enum values, which is real behavioral context. However, it doesn't state whether it's read-only, whether it triggers any mutation (e.g. marking a quest started), or whether it consumes a daily check-in.

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 front-loaded sentences. The mechanism detail is directly useful for understanding why the result is stable within a day.

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?

An output schema exists, so return structure need not be explained. The deterministic-selection rule is the key non-obvious fact and is included. Missing side effects and sibling differentiation keep it from a 5.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents car_id, including the default resolution behavior (TESLA_CAR_ID env or first car). The description adds nothing about 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?

States a specific verb+resource: checks today's daily driving quest and its progress. The mechanism sentence clarifies the resource further. It doesn't differentiate from the closest sibling (check_driving_achievements), which is a conceptually adjacent gamification tool, so it falls short of a 5.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No when-to-use, when-not-to-use, or alternatives are given. An agent cannot tell whether this overlaps with check_driving_achievements or how quests differ from achievements. No prerequisites or context is provided.

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

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

No annotations are provided, so the description carries the full behavioral burden. It implies a read-scan over recent drives, but says nothing about what an 'achievement' is, whether checking has side effects (e.g. persisting/unlocking badges), or any auth/rate-limit 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?

A single compact sentence that front-loads the action and scope with no wasted words. It is efficient, though its brevity borders on under-specification rather than genuine conciseness.

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?

An output schema exists, so return-value explanation is not required, and the schema fully documents inputs. Still, with no annotations and an undefined 'achievements' concept, the definition leaves the agent without enough context on the tool's behavior and scope to be fully confident.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so both parameters (days, car_id) are fully documented in the schema, including defaults and the env fallback for car_id. The description adds no additional parameter meaning, which is the correct baseline of 3 when the schema does the work.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb (scan/check) and resources (recent drives and charging sessions) tied to 'driving achievements', so the agent can tell it apart from pure data-fetch siblings like tesla_drives or tesla_charges. However, it does not distinguish itself from conceptually adjacent siblings such as check_daily_quest or get_driver_profile, and 'achievements' is left undefined.

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?

There is no when-to-use guidance, no exclusions, and no named alternative. The agent cannot tell from the text whether this is the right tool versus check_daily_quest, get_driver_profile, or tesla_driving_score; usage is only implied by the verb 'check'.

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 does not state whether the report is saved to a file, returned as text, the format details beyond Markdown and Emoji, whether it requires authentication, or whether it has any side effects. For a report generation tool with no annotations and an output schema, this is a significant gap.

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?

A single, efficient sentence that is front-loaded and wastes no words. It is appropriately sized for a simple report generation tool, though it could be slightly more informative without losing conciseness.

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

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

Given no annotations, an output schema, and several similar monthly report siblings, the description is incomplete. It does not clarify the tool's uniqueness, its output format beyond 'Markdown with Emoji', or any behavioral traits. An agent would struggle to choose this over tesla_monthly_report or tesla_monthly_summary.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already fully documents all three parameters including defaults and formats. The description adds no parameter information beyond what the schema provides. Baseline 3 is appropriate when the schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb+resource: 'Generate a polished Markdown monthly driving report with Emoji.' The agent knows it produces a monthly driving report in Markdown. However, it does not distinguish from siblings like tesla_monthly_report or tesla_monthly_summary, leaving ambiguity about which monthly report tool to use.

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?

There is no guidance on when to use this tool versus tesla_monthly_report, tesla_monthly_summary, or other reporting tools. No prerequisites or context are provided. The description only states what it does, not when it is appropriate.

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 behavioral burden. It does not indicate whether the operation is read-only, the granularity of extracted data, pagination behavior, or any constraints. It does say it extracts structured drive and stop data, which is a small behavioral hint, but overall leaves significant gaps for a data-extraction tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two short sentences, front-loaded with the core purpose and followed by a clarifying detail. It is efficient with no wasted words, though the second sentence is slightly redundant with the first.

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

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

The tool has an output schema, so the return format need not be explained, and the schema fully covers parameters. However, with no annotations and no usage guidance, the description is only minimally adequate for an agent to confidently select and invoke this tool among many similar siblings.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents all three parameters including the default behavior for car_id. The description adds little parameter-level detail beyond 'within a time window', so the baseline of 3 for high schema coverage applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states the tool generates a travel narrative timeline and extracts drive and stop data within a time window, which is specific enough. However, it does not distinguish this from siblings like tesla_drives or generate_monthly_driving_report, leaving the agent to infer when this narrative-focused aggregation is preferred.

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?

There is no explicit when-to-use guidance, no exclusions, and no named alternatives. The phrase 'for LLM-powered travel blogging' hints at the use case but does not tell the agent when to select this over tesla_drives or other trip 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 burden, and it does disclose the actual selection logic: places visited exactly once within the lookback, filtered by min_stay_hours. That is meaningful behavioral context for a computed/read-only tool, though it never explicitly says the operation is read-only or what permissions/side effects apply.

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 tight sentences, front-loaded with the core outcome and followed by the selection criteria. The 'memory blindbox' metaphor is slightly loose but does not waste space or bury the point.

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 3-param, no-required-arg analytics tool with an output schema handling return format, the description covers purpose, selection algorithm, and trip-recommendation intent. Only the read-only nature and relationship to sibling destination tools are left implicit.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents car_id, min_stay_hours, and months_lookback. The description restates min_stay_hours and 'lookback period' but adds no syntax, units, or defaulting nuance beyond 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?

States a specific outcome (find rare one-time destinations visited only once, with a minimum stay) rather than restating the name. An agent can tell it produces surprise weekend-trip candidates, though it never names which sibling (e.g., tesla_top_destinations) it replaces.

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?

Implies usage through 'good candidates for a surprise weekend trip recommendation' and the 'memory blindbox' framing, but gives no explicit when-to-use/when-not or any alternative tool for plain destination lookups. Usage is inferable, not 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?

No annotations are provided, so the description carries the full behavioral burden. It does not disclose permissions, whether it is read-only (implied by 'get' but unstated), what 'physical parameters' are returned, or the default-resolution behavior (env fallback / latest session). The sommelier framing adds no behavioral context.

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?

Very short, which is efficient, but the second sentence about LLM roleplay does not earn its place in a tool description and distracts from the actual contract. Front-loading the verb+resource is good.

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?

An output schema exists, so return values need not be explained. However, for a read tool with no annotations and heavy sibling overlap, the description is too thin to route an agent reliably and omits any disambiguation from the other charging tools.

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 the schema fully documents both parameters including defaults and the 'latest' fallback. The description adds nothing beyond the schema. Baseline 3 is appropriate when the schema does all the work.

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?

States a specific verb and resource: get physical parameters for a single charging session. But 'vintage data' is undefined jargon, and the description offers no differentiation from the many sibling charging tools (tesla_charging_history, tesla_charges, tesla_charging_by_location). The 'sommelier' framing muddies rather than clarifies purpose.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No when-to-use guidance at all. With dozens of charging-related siblings, the description never states when to pick this over tesla_charges or tesla_charging_history. The 'sommelier roleplay' line is stylistic direction, not usage 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 are provided, so the description carries the full burden. It does disclose useful behavioral traits — the returned fields and the 160,000 km Easter egg trigger — but never states that this is a read-only operation, nor any auth/permission needs. It adds real context but does not fully cover the behavioral profile.

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?

Front-loaded and readable, but the trailing 'Returns JSON with: current_rank, total_distance_km...' duplicates content already conveyed by the output schema, so not 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?

With an output schema present, the description need not (and largely need not have) re-listed return fields, yet it still conveys purpose and content adequately. The only meaningful gap is the absence of any routing guidance among the many sibling tools.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100% and the single car_id parameter is fully documented in the schema with its default-resolution logic. The description adds nothing about car_id, so the baseline of 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ('Get driver gamification profile') and enumerates the exact content (rank, milestones, Easter eggs), so the agent knows what it retrieves. However, it never distinguishes itself from conceptually overlapping siblings like check_driving_achievements, check_daily_quest, or tesla_driving_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?

There is no when-to-use, when-not-to-use, or alternative-tool guidance. With several gamification-adjacent siblings (check_driving_achievements, get_vehicle_persona_status, check_daily_quest), the agent is left to infer which one to call.

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 partially compensates by disclosing the computation (window functions on charging_processes) and the returned fields, but it does not say whether this is a read-only aggregate, what permissions/defaults apply, or how cost is measured across 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?

Front-loaded with the core result, followed by the method and the return fields. Slightly padded by the implementation detail about window functions, which is interesting but not necessary for invocation.

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?

An output schema exists, yet the description redundantly enumerates return fields rather than trusting the schema. Combined with clear purpose and full parameter coverage, an agent has enough to call it correctly; the missing piece is any note on read-only safety or empty-result behavior.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100% and the single car_id parameter is fully documented in the schema, including its default resolution behavior. The description adds no parameter meaning beyond that, which matches the baseline for full schema coverage.

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?

States a precise verb and resource with a differentiating constraint: the distance must be 'between two consecutive charges', which separates it from generic trip aggregations. However, it never names or contrasts with the sibling 'tesla_longest_trips', leaving that overlap ambiguous.

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?

There is no explicit when-to-use guidance, no prerequisites, and no reference to an alternative tool. Usage is only implied by the purpose sentence, which is thin given several overlapping 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 disclosure burden. It does reveal the concrete outputs (idle ratio, longest continuous drive, max speed, vampire drain), which adds real behavioral context, but it says nothing about permissions, read-only nature, or analysis scope/windowing behavior beyond what the schema shows.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two short sentences with the core purpose front-loaded; the second sentence adds value by naming the concrete metrics. Slightly informal 'vampire drain'/'personality' framing but no filler.

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

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

An output schema exists, so return values need not be explained, and all four parameters are documented in the schema. The description supplies the intent and the metric highlights, making it adequate for an agent to select and call the tool, though it gives no guidance on temporal precedence among the parameters.

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% -- year, month, car_id, and days_lookback are all documented with defaults and interaction rules (month requires year; days_lookback used when year/month unset). The description adds no parameter information beyond the schema, so the baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description gives a specific verb+resource (vehicle persona status) and enumerates the metric categories it contains (activity, fatigue, extremes, health). It is clear what the tool does, but it does not distinguish itself from near-neighbors like tesla_status or get_driver_profile, so it stops short of a 5.

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 line about providing 'structured metrics for an LLM to roleplay a "vehicle with personality"' implies the intended use case, but there is no explicit when-to-use, when-not-to-use, or named alternative among the many sibling tools (tesla_status, get_driver_profile, tesla_driving_score). Usage is only implied.

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 disclosure burden. It usefully states the output shape (monthly snapshots of ideal range) and the temporal nature of the data, which hints at a read-only historical tool. However, it does not confirm read-only semantics, permissions, or how far back the trend extends.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two short, front-loaded sentences with no wasted words. The second sentence partially restates the first ('range at 100% charge' vs 'ideal range when battery is at 100%'), a minor redundancy, but overall 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?

With an output schema present, return-value explanation is not required, and the single optional parameter is fully covered by the schema. For a simple read-only trend tool the description is mostly sufficient, though it omits any usage or scope guidance.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

There is a single optional parameter with 100% schema description coverage, so the schema fully documents car_id's filtering and default behavior. The description adds no parameter meaning beyond that, making the baseline 3 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 states a specific resource and metric: a battery degradation trend measured as range at 100% charge over time, with monthly snapshots. This is clearly distinct from charging or status siblings. It doesn't explicitly name or differentiate itself from any alternative, but no close sibling overlaps this specific health 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?

There is no guidance on when to use this tool versus alternatives such as tesla_status or tesla_charging_history, and no mention of any prerequisites. The agent must infer usage context entirely from the resource name.

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

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

With no annotations, the description carries the full burden, but this is an inherently safe zero-parameter read, so there is little risk to disclose. It does describe the returned fields (ID, name, model, VIN, efficiency), though that overlaps with the output schema. No auth, rate-limit, or error behavior is mentioned.

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 short sentences, front-loaded with the core action and ending on actionable usage guidance. The middle sentence listing return fields is partly redundant given an output schema exists, but it is brief and does no real harm.

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 zero-parameter read tool with an output schema and no annotations, the description covers purpose, returned content, and the downstream car_id handoff. Little more is required for an agent to invoke it correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool takes zero parameters, so the baseline is 4. There is nothing for the description to clarify beyond what the empty schema already conveys.

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?

States a specific verb and resource — 'List all vehicles registered in TeslaMate' — and names the identity fields returned. It does not explicitly contrast itself with any sibling, but its purpose as the vehicle-enumeration entry point is unambiguous among the many analytical Tesla 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 closing sentence 'Use the car_id with other tools to query a specific vehicle' gives workflow context, implying this tool should be called first to obtain IDs. However, it never states when to use this versus alternatives or any prerequisite conditions, so guidance is implied rather than explicit.

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 behavioral burden. It does not disclose whether this is read-only, whether it requires auth, pagination behavior, rate limits, or what the response contains beyond 'location, energy, cost breakdown'. An output schema exists, which offsets return-value disclosure, but the read-safety and operational context 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 tight sentences with the primary description front-loaded and the sibling distinction second. No filler, though it is on the sparse side for a tool with a rich sibling set.

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?

Adequate for a read-style query tool: it covers purpose, content dimensions, and sibling relationship, and an output schema fills in return values. However, with zero annotations and no notes on auth, read-only nature, or pagination, it leaves behavioral gaps for an agent to infer.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so all five parameters (days, limit, car_id, end_date, start_date) are already documented in the schema. The description adds no parameter-level detail. Baseline 3 is appropriate when the schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb+resource ('Detailed charging sessions') and enumerates the content (location, energy, cost breakdown). It explicitly distinguishes itself from sibling tesla_charging_history by contrast (granular session data vs. summarized by process), which is strong 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?

It names tesla_charging_history as the alternative and implies this tool is for granular per-session data, but stops short of explicit when/when-not conditions. The contrast gives usable routing context but is not a full usage directive.

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 exist, so the description carries the full behavioral burden, and it discloses nothing: no indication of whether the grouping returns per-car totals, what location granularity means, whether results are ordered, or any limits or permissions. A single noun phrase is far too thin 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?

One short, front-loaded sentence with no padding or repetition. It is efficient, though so minimal that the terseness borders on under-specification rather than deliberate concision.

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?

An output schema exists, so return values need not be explained, and both parameters are fully documented, which covers much of the burden for a simple two-parameter aggregation. However, with no annotations and no routing guidance against the many charging-related siblings, an agent still cannot confidently decide when to call this tool.

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%, with both days and car_id fully documented including defaults and the TESLA_CAR_ID fallback, so the schema does the heavy lifting. The description adds no parameter meaning beyond what is already there, making the baseline 3 correct.

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 phrase names a specific resource (charging sessions) and a specific aggregation (grouped by location), which is enough to separate it from raw-log siblings like tesla_charges and tesla_charging_history. It lacks an explicit verb and never names those siblings, so it falls short of a 5.

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?

There is no when-to-use guidance, no prerequisites, and no mention of the alternative tools an agent should pick instead. The only usage signal is the implied notion that an aggregated view is wanted, which the agent must infer.

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

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

With no annotations, the description carries the full behavioral burden. It implies a read-only historical query and enumerates the returned fields (energy added, duration, battery range, location), which is useful signal, but it says nothing about permissions, vehicle-selection behavior when car_id is omitted, or data availability limits.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two short sentences, front-loaded with the scope and then the returned content; every clause earns its place. It could be marginally tighter, but there is no waste or padding.

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?

An output schema exists, so return values need no elaboration, and the parameters are fully covered by the schema. What is missing for an unannotated read tool with many similar siblings is any differentiation or usage context, leaving the definition minimally viable rather than complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so days, start_date, end_date, and car_id are already fully documented in the schema, including defaults and the start_date-overrides-days rule. The description only echoes the 'last N days' framing and adds nothing about the date-range or car_id semantics 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?

States a specific verb and resource ('Charging sessions over the last N days') and names the fields returned, so an agent knows this is a historical charging log rather than a live state read. It does not, however, distinguish itself from close siblings such as tesla_charges, tesla_charging_by_location, or get_charging_vintage_data, which is the main remaining gap.

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?

There is no when-to-use guidance, no mention of prerequisites (e.g., a vehicle must be configured), and no routing to alternatives like tesla_charges for live charging state or tesla_charging_by_location for aggregated location breakdowns. The agent must infer the use case purely from the name.

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

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

No annotations provided, so the description must carry behavioral burden. It does not state whether this is a read-only operation, its performance characteristics, permissions, or return format (though output schema exists). Only adds minimal context about energy consumption.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two short sentences, front-loaded with the resource. The second sentence ('Shows driving activity with energy consumption') is somewhat redundant with the first, but overall 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?

For a 4-parameter query tool with full schema coverage and an output schema, the description is minimally adequate. However, it lacks usage context and behavioral disclosure, which are needed without annotations, leaving gaps for an agent to choose and invoke correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so parameter details are fully documented in the schema. The description adds nothing about parameters, but baseline is 3 when schema covers them.

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?

States it returns recent drives with distance, duration, efficiency, and locations, which is a specific resource and set of attributes. It distinguishes from siblings only by name; no explicit contrast with other drive-related tools like tesla_longest_trips or tesla_driving_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?

Provides no when-to-use guidance or alternatives. Does not indicate when to choose this over other drive or trip tools, nor any prerequisites or time-range specifics beyond the schema.

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 score composition but omits whether the operation is read-only, permission requirements, return format, or how period modes affect behavior, leaving 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 a single, front-loaded sentence with no wasted words. It is appropriately sized for a brief tool summary, though the fragment style slightly reduces structural 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?

An output schema exists, so return values need not be described. However, given eight optional parameters with multiple period modes and no annotations, the description is incomplete: it does not explain how to choose among period settings or when this score tool is preferable to sibling tools.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so all eight parameters are documented directly in the schema. The description adds no parameter semantics beyond that, making the baseline score of 3 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 names a specific resource, the driving score, and its inputs (acceleration, braking, speed habits). It lacks an explicit verb like 'retrieve' and does not differentiate the tool from siblings such as tesla_monthly_report or check_driving_achievements, but the core purpose is clear.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is given on when to use this tool versus alternatives, nor are prerequisites or exclusions mentioned. The description only states what the score is based on, leaving the agent to infer usage context.

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

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

Annotations are absent, so the description carries the full behavioral burden. It does disclose the metric (Wh/mi), the aggregation window (weekly averages), and the data source (driving data), which helps the agent interpret output. However, it says nothing about auth requirements, data availability behavior, or empty-result 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?

Two short sentences, front-loaded with the metric and resource, zero filler. Every clause adds 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?

An output schema exists so return values need not be described, and the schema fully covers parameters. What is missing is the routing context: with three closely related efficiency tools, the description should say when this plain variant is preferred over the temp/weather breakdowns.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so all four parameters (days, car_id, end_date, start_date) are already documented in the schema, including defaults and override behavior. The description adds no parameter-level meaning beyond that, so baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific resource and metric: energy consumption trends in Wh/mi, aggregated as weekly averages from driving data. Clear what the tool computes, but it does not distinguish itself from close siblings like tesla_efficiency_by_temp or tesla_efficiency_by_weather.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No explicit when-to-use guidance and no alternatives named. An agent must infer from the name that this is the default overall efficiency view versus the temperature/weather variants. Implied usage only.

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 behavioral burden, and it discloses almost nothing: not whether data is cached/live, the time window, granularity, or units beyond Wh/mi. It is purely a restatement of the concept.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two short sentences, front-loaded with the resource and unit. Not wasteful, though the second sentence largely restates the first ('efficiency by temperature' = 'how temperature affects consumption').

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?

An output schema exists, so return values needn't be explained, and the lone parameter is documented. But with no annotations and a sibling overlap (by_weather), the description is only minimally complete for correct tool 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 description coverage is 100% and the single optional car_id is fully documented in the schema, including the env-var fallback. The description adds no parameter meaning beyond that, so baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific resource (efficiency curve by temperature, expressed as Wh/mi) with a clear measurement unit. It does not name or differentiate from the very similar sibling tesla_efficiency_by_weather, which an agent selecting between the two would need.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No when-to-use guidance and no mention of the near-identical sibling tesla_efficiency_by_weather or the broader tesla_efficiency tool. The agent is left to infer selection from the name 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 burden, and it does so well: it discloses the no-database-writes behavior, the API rate-limit mitigation via sampling (TESLA_WEATHER_SAMPLE_MAX default 60), caching, and required env vars with a graceful fallback. It stops short of describing output shape, but an output schema exists to cover returns.

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?

Front-loaded purpose statement followed by a clear contrast, then a numbered method and a limits note. The method list is slightly long but each step earns its place by explaining the sampling/caching pipeline. No redundant filler.

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

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

For a multi-step analysis tool with external API dependencies, the description covers the method, constraints (sampling, caching, API keys), and result grouping. An output schema exists so return values need not be explained. Nothing critical to correct invocation is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the two parameters (days, car_id) are already documented in the schema with defaults and env fallbacks. The description adds contextual detail about sampling and env-based car selection but nothing parameter-specific beyond what the schema provides. 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?

States a specific analysis (efficiency grouped by weather condition) and explicitly contrasts with the sibling tesla_efficiency_by_temp, noting it buckets by actual weather rather than just outside_temp. An agent can distinguish this tool from its closest sibling without opening either schema.

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?

Points to the alternative (tesla_efficiency_by_temp) and explains the differentiating condition (weather condition vs temperature bucket). It also surfaces prerequisites (QWEATHER_API_KEY/HOST). However, it does not state explicit when-not-to-use conditions or when the temp-based tool is preferable.

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

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

With no annotations, the description carries full burden and does disclose meaningful traits: the data source (TeslaMate positions table), the polling cadence caveat ('polled periodically, not real-time'), and explicitly omitted fields (sentry mode, lock status, media). Auth requirements and rate limits are still unaddressed.

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 tight sentences with the core purpose front-loaded and the freshness/omission caveats grouped after. No filler 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?

An output schema exists, so return values need not be explained. The description supplies the essential context an agent needs – source, staleness, and what is missing – though it leaves the relationship to the similarly-named tesla_status sibling unstated.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100% and the single car_id parameter is already fully documented, including its default resolution order. The description adds no parameter-level detail, so the baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource – the latest polled vehicle state – and enumerates the domains covered (battery, climate, location). It is distinguishable from the history siblings (tesla_state_history, tesla_location_history) by the word 'latest', but it does not explicitly name tesla_status, which likely overlaps.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is implied rather than stated: 'latest polled' suggests current-snapshot use vs the history tools, and the 'not real-time' note implies when this data is unsuitable. However, no sibling is named as an alternative and no explicit when/when-not guidance 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 supplied, so the description carries the disclosure burden. It usefully explains the clustering/proximity-grouping behavior and that output is time-at-cluster, but says nothing about permissions, empty-data behavior, or result ordering. Adds genuine behavioral value beyond the schema while leaving 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 tightly written sentences with zero filler. The core identity ('where the car has been') is front-loaded and the mechanism follows immediately.

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

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

With an output schema present, return values need not be explained, and params are fully covered in the schema. The description covers the computation adequately, though it omits the days-vs-start_date override nuance and any routing guidance among similar location tools.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so all four parameters (days, car_id, start_date, end_date) are already documented in the schema. The description adds no parameter-level meaning, which is acceptable given the schema does the work, hitting the baseline.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a concrete verb and resource ('where the car has been', 'top locations by time spent') plus the mechanism ('groups positions by proximity'). Clear on its own, but it does not distinguish itself from close siblings such as tesla_top_destinations or tesla_charging_by_location, which an agent could easily confuse it with.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No indication of when to reach for this tool versus tesla_top_destinations, tesla_charging_by_location, or tesla_state_history. There is no stated precondition or exclusion, so the agent must infer selection purely from the name.

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

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

No annotations are provided, so the description carries the full burden. It doesn't say whether this is read-only, how ranking ties are handled, or what the return shape includes, though an output schema exists. 'Your epic road trips' is marketing, not behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two short clauses, front-loaded with the core action. No wasted sentences, though the second clause is decorative rather than informative.

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

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

With an output schema present, return values needn't be explained, but the description is thin for a ranking tool with no annotations and several similar siblings. It leaves the differentiation burden entirely on the name.

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 both parameters are documented in the schema. The description adds nothing parameter-specific, which meets the baseline of 3.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific resource and ranking criterion (drives ranked by distance), which is clear enough. However, 'Top drives ranked by distance' is vague about whether it ranks drives overall, per trip, or per vehicle, and it doesn't distinguish itself from siblings like tesla_drives or get_longest_trip_on_single_charge.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this versus tesla_drives, tesla_trips_by_category, or get_longest_trip_on_single_charge. The agent is left to infer selection from the name 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 behavioral burden. It implies a read-only aggregation but never states permissions, cost of the call, whether data is live or cached, or what timezone/aggregation boundaries apply; with an output schema present the return content is partly covered, but call-time behavior is not.

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?

A single front-loaded sentence with no filler or repetition. It is efficient, though its brevity is partly under-specification rather than deliberate compression.

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

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

With an output schema the return shape needn't be explained, and the schema documents all three parameters. What is missing is the disambiguation against near-identical monthly siblings and any behavioral context, which the description leaves entirely to the agent to infer.

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%, including the car_id fallback to TESLA_CAR_ID or the first car, so the schema already does the heavy lifting. The description adds no extra meaning about year/month handling or the car_id default, so the baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a specific resource (monthly driving report) and its contents (stats plus month-over-month comparison), which is better than a tautology. However, it does nothing to distinguish itself from very close siblings like tesla_monthly_summary and generate_monthly_driving_report, leaving an agent unable to choose between them from the description alone.

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?

There is no guidance about when to use this tool versus the many overlapping siblings (monthly summary, monthly driving report generator, savings, efficiency breakdowns). The only implied usage is 'when you want a monthly report,' which is circular.

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 behavioral burden, and it does disclose derivation semantics that matter for interpreting results: drive kWh is estimated from range-drop, charge kWh from sessions, vampire kWh from parked drain, and Wh/km is computed from driving kWh only so efficiency is not contaminated. It omits auth/permission needs, cost, and side-effect profile, so it is not complete, but the estimation methodology is unusually substantive.

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?

Front-loaded purpose sentence followed by a scannable bulleted breakdown of the three categories, with the Wh/km caveat last. Every line carries information; only the parenthetical Chinese labels are arguably redundant, which keeps it just under 5.

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?

An output schema exists, so return-shape explanation is unnecessary, and both parameters are schema-documented. Given the tool's semantic complexity, the description supplies the crucial unit-definition context needed to use the output correctly; only cross-tool routing is missing.

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% and both parameters (car_id default resolution, months default/max) are already fully documented in the schema. The description adds no parameter-level meaning beyond that, so the baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb+resource+scope: a monthly driving summary broken into three named energy categories, each with its source. An agent knows exactly what the tool produces. It stops short of 5 because it never distinguishes itself from close siblings such as tesla_monthly_report and generate_monthly_driving_report.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is implied by the name and the month-counting parameter, but the description never says when to prefer this over tesla_monthly_report, tesla_drives, or tesla_vampire_drain, nor any prerequisites. No explicit when/when-not guidance is present.

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

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

With no annotations, the description carries the full behavioral burden, and it does not say whether this is a read-only computation, whether it needs any prior data (charging history), or how env-var fallbacks behave. It implies a safe read via 'savings scorecard', but that is inference rather than disclosure.

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?

A single front-loaded clause with zero waste. It is arguably too terse for a tool with sibling overlap, but nothing in it is padding.

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?

An output schema exists and params are fully covered, so return values and inputs need no prose. What is missing is the routing context: when this beats calculate_eco_savings_vs_icev or tesla_trip_cost. Adequate but with a clear gap for a 3-param tool in a dense sibling set.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so car_id, gas_price, and mpg_equivalent (including their env-var defaults) are already fully documented. The description adds no parameter meaning beyond the schema, which is the baseline-3 case.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a clear resource and scope: a gas-savings scorecard comparing running cost against a gas car. An agent can grasp what the tool computes, but nothing distinguishes it from the near-identical sibling calculate_eco_savings_vs_icev, so it fails the sibling-differentiation bar for a 5.

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?

There is no when-to-use guidance, no prerequisites, and no mention of the closest alternative (calculate_eco_savings_vs_icev) despite obvious overlap. The agent must infer usage entirely from the name.

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

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

No annotations are provided, so the description carries the full burden. 'History' and 'all recorded' imply a read-only, unfiltered listing, which is useful, but the description says nothing about pagination, ordering, or the fact that the schema actually supports filtering by vehicle.

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?

One short sentence, front-loaded with the resource. There is mild redundancy between 'firmware version history' and 'all recorded software versions', but nothing is wasted 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?

An output schema exists, so return values need not be explained, and the single parameter is documented. What is missing is any routing against the sibling 'tesla_version' and any note on ordering or scope limits, which leaves the definition merely adequate.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100% and the single car_id parameter is fully documented in the schema, including its env-var default. The description adds nothing about the parameter, so the baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific resource and what it contains ('firmware version history -- all recorded software versions and install dates'), which is more than a restatement of the title. It does not, however, distinguish itself from the sibling 'tesla_version', which an agent could easily confuse with a firmware-version tool.

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?

There is no when-to-use guidance, no exclusions, and no mention of the near-synonymous sibling 'tesla_version'. The agent must infer the intended scope entirely on its own.

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

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

With no annotations, the description carries the full behavioral burden, and it does disclose the semantic content of the result (state transitions between awake and sleeping). It says nothing about permissions, data freshness/rate limits, or granularity, so it is only partially transparent for a history-read tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two short sentences, front-loaded with the returned resource, with the interpretive sentence second. The vampire-drain sentence earns its place by giving intent, though 'online, asleep, offline' and 'awake vs sleeping' restate the same idea.

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?

An output schema exists, so return-shape explanation is unnecessary and parameters are fully documented. What is missing is the relationship to the close sibling tesla_vampire_drain, which an agent must choose between; without it the definition is adequate but not 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%, with defaults and the start_date-overrides-days interaction already documented in the schema. The description adds no parameter meaning beyond that, so the baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a specific resource (vehicle state transitions) and enumerates the values it reports (online, asleep, offline), so an agent knows exactly what data this returns. It stops short of distinguishing itself from sibling tesla_vampire_drain, which it actually points at by citing 'vampire drain analysis' as the use case.

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 gives one implied use case ('useful for vampire drain analysis'), which tells the agent something about intent. However, it never states when to prefer this over tesla_vampire_drain (which appears to compute drain directly), tesla_status, or tesla_live, and gives no exclusions or preconditions.

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

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

No annotations are provided, so the description carries the burden. It discloses the returned field categories and that it is a 'latest position snapshot', which is useful, but says nothing about freshness/staleness, auth requirements, or whether the data is cached versus live. It adds moderate context beyond a bare purpose statement.

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 tightly written sentences that front-load the resource and its fields. No filler; the only mild redundancy is the second sentence restating content already implied by the first.

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 low-complexity, single-optional-parameter read tool with an output schema present, the description explains what is returned without needing to detail return values. The key gap is the lack of differentiation from tesla_live/tesla_state_history, which matters 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?

Only one optional parameter (car_id) exists and schema description coverage is 100%, so the schema already documents the ID filter and its default behavior. The description adds no meaning beyond that, which is acceptable baseline given full schema coverage.

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?

States a specific resource set ('Current vehicle state') and enumerates the fields returned (battery, range, location, climate, odometer), plus the source (TeslaMate). However, it does not differentiate from likely-overlapping siblings such as tesla_live or tesla_state_history, so the agent cannot fully disambiguate from the text alone.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No when-to-use, when-not-to-use, or alternative guidance is given. With ~37 sibling tools including tesla_live and several history/status tools, the absence of routing guidance leaves the agent to guess which 'current state' tool to call.

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 the description carries the full burden. It does not mention how visits are counted, over what timeframe, whether it reads live data, or the auth/vehicle context implied by the car_id default. Only the ranking basis is disclosed.

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?

A single front-loaded sentence with no filler. Every word 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?

An output schema exists so return values need not be explained, and annotations are absent. What's missing is query scope (time window, visit definition) and any guidance on choosing this over the many location-related siblings.

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 both params (limit, car_id) are fully documented in the schema including defaults, max, and env fallback. The description adds no parameter detail, so the baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb (ranked) and resource (most visited locations) with the ranking metric (number of visits). Clear on its own, though it doesn't explicitly differentiate from sibling tesla_charging_by_location or tesla_location_history, which could also surface locations.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No indication of when to use this vs. tesla_location_history, tesla_charging_by_location, or tesla_longest_trips. The agent must infer the difference between 'most visited destinations' and 'location history' on its own.

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

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

With no annotations, the description carries the full disclosure burden and does add a genuinely useful behavioral trait: data is only recorded during drives/charging, not while parked. This is real context that prevents misinterpretation of sparse results. It stops short of stating read-only nature or any auth/rate-limit 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?

Three tight sentences, front-loaded with the core purpose, then output content, then the key caveat. Every sentence earns its place and nothing is padded.

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?

Output schema exists, so return values needn't be spelled out, and the data-availability caveat covers the main trap for a read-only history tool. It is largely complete, though it would benefit from explicit sibling routing to tesla_tpms_status.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so all four parameters (days, car_id, start_date, end_date) are already documented in the schema, including the start_date override of days. The description adds no syntax or format detail beyond that, so the baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb+resource (TPMS pressure history) and clarifies the data source (TeslaMate positions table) plus what it reports (average and min/max pressures). The 'history' framing implicitly distinguishes it from the sibling tesla_tpms_status, but it never names or contrasts that sibling explicitly.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is only implied through the 'history' scope and the caveat about when data exists. There is no explicit when-to-use/when-not guidance and no routing to alternatives such as tesla_tpms_status for current readings, so the agent must infer the 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?

No annotations are provided, so the description carries the full behavioral burden. It transparently discloses the data source ('latest position record in TeslaMate') and the warning logic, including threshold sources and the >0.15 bar deviation rule. It does not cover auth requirements or failure modes, but this is a read-only status tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with purpose, then adds source and warning criteria in compact sentences. Every sentence earns its place by explaining behavior the agent needs to interpret the output correctly.

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

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

Given the low complexity, 100% schema coverage, and an output schema, the description supplies enough context: what is returned, where it comes from, and how anomalies are identified. No critical agent-facing detail is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema has 100% description coverage for the single car_id parameter, so the baseline is 3. The description does not add any parameter meaning beyond the schema, such as default resolution behavior or filtering 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 states a specific resource ('TPMS pressures') and temporal scope ('Current'), which distinguishes it from the sibling tesla_tpms_history. An agent can tell this tool reports present tyre pressure status rather than historical 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 implies usage through 'Current TPMS pressures,' but it does not explicitly say when to choose this over tesla_tpms_history or other status tools. No when-not or alternative guidance is provided.

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

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

With no annotations provided, the description carries the full burden. It implies a read-only aggregation but never says so, and gives no information on time window ('recent'), scoping defaults, or whether counts are per-day/per-trip.

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?

One efficient sentence with the core action front-loaded. No padding or repetition of structured fields.

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?

An output schema exists, so return values need not be explained. For a zero-annotation tool the description is only minimally complete: it never defines what counts as a category or a recent drive, which an agent could need.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the single car_id parameter is already fully documented including its env-var default. The description contributes nothing beyond the schema, so baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource: shows trip counts grouped by category, scoped to recent drives. Clear enough, though the phrase 'recent drives' is undefined and there's no explicit contrast with the close sibling tesla_trips_by_category.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No when-to-use guidance at all. There is a directly overlapping sibling, tesla_trips_by_category, and the description gives no condition that would let an agent choose between them.

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

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

With no annotations the description carries the full burden, and it does disclose meaningful behavior: the estimate derives from a personal 30-day average efficiency and current battery level, and weather is display-only. It omits whether the call is side-effect free, permission/env requirements for the fallback defaults, and failure behavior for un-geocodable destinations.

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?

Purpose and outputs are front-loaded in the first line, followed by methodology and the weather caveat. Every sentence carries information; the weather note is slightly longer than needed but prevents a real misconception, so it 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?

An output schema exists, so return values need not be explained, and the description covers purpose, data sources, and a caveat. What is missing is usage routing against the many savings/efficiency siblings, which leaves a modest gap for a tool in this crowded family.

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 all four parameters (car_id, gas_price, mpg_equivalent, destination) are already documented, including their env-var defaults. The description adds no parameter-level meaning, which is the correct baseline of 3 when the schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Specific verb (estimate) plus resource (trip cost to a destination) and an enumeration of the outputs (kWh, cost, range check). An agent can distinguish this from compute-only siblings like tesla_efficiency, though it never names the closest alternatives (tesla_savings, calculate_eco_savings_vs_icev).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is implied by the required 'destination' argument and the trip-planning framing, but there is no explicit when-to-use guidance, no prerequisites, and no exclusions naming which sibling to prefer for savings or historical-trip questions. Adequate but with clear gaps.

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 behavioral burden and discloses essentially nothing: no note that it is a read-only query, no pagination/limit behavior, no default date-window behavior, no permission requirements. An output schema exists, which covers return shape, but invocation-side behavior is left implied by the word 'Get'.

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?

A single front-loaded sentence with zero filler and no repetition of the title or schema. It is efficient, though economy here shades into under-specification rather than true tightness.

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 rich schema and existing output schema mean parameters and return values are adequately covered elsewhere, so the description does not need to restate them. What remains missing is the sibling-boundary information (category enumeration vs. category filtering) that makes this definition minimally viable rather than 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% and all six parameters (days, limit, car_id, category, end_date, start_date) carry their own documented defaults and semantics, so the schema does the heavy lifting. The description adds nothing beyond restating the category filter, which is the baseline 3 case.

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?

States a specific verb ('Get') and resource ('trips') with the filtering dimension ('by category'), so the core operation is unambiguous. However, it makes no attempt to distinguish itself from close siblings like tesla_drives, tesla_trip_categories, or tesla_longest_trips, which an agent must otherwise infer.

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?

There is no when-to-use guidance, no exclusions, and no mention of alternatives. An agent cannot tell from the text why it should pick this over tesla_trip_categories (likely the companion that enumerates available categories) or tesla_drives without opening both schemas.

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

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

With no annotations, the description carries the full burden and does well: it discloses the 8-hour parked window, the camping-mode tagging rule and threshold, the fixed 75 kWh reference battery so the threshold is car-size-invariant, and the important limitation that Sentry/third-party activity is conflated with camping use. It omits only operational traits such as whether this is purely read-only and any permission or rate-limit considerations.

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?

Front-loaded with the one-line purpose, then the threshold and caveats in dense but necessary prose. Every sentence earns its place: the window length, threshold, fixed reference battery, and Sentry caveat all change how an agent interprets the output.

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?

An output schema exists, so return values need not be explained, and the description supplies the interpretive context (what 'camping mode' means, why the threshold is fixed, what caveats apply). It is essentially complete for a read-only analysis tool, lacking only an explicit read-only/permission statement.

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% (days, car_id, start_date, end_date each documented), so the schema does the parameter work and the description adds no syntax or format detail beyond it. Baseline 3 applies. Notably, the description's start/end defaults and car selection are left entirely to the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The first line states a specific verb and resource: vampire drain analysis of battery loss while parked overnight. It is clearly distinguishable from siblings like tesla_battery_health or tesla_state_history because it names the overnight-parked-loss phenomenon and the 8+ hour parked-window criterion.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is implied by the framing (analyze overnight parked battery loss, with camping-mode tagging), and the caveat that Sentry/third-party drain is not distinguished tells the agent when results may mislead. However, it never explicitly names an alternative tool (e.g. battery_health, state_history) or states when-not to use this one.

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

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

With no annotations, the description carries the behavioral burden and discloses what the tool returns, including server version, Python/fastmcp versions, timezone, tool count, and database reachability. It implies a read-only diagnostic operation, though it does not explicitly state read-only safety or auth requirements.

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, front-loaded with the primary purpose and followed by concrete return values and use cases. Every sentence earns its place 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?

For a zero-parameter diagnostic tool with an output schema, the description provides enough context to invoke it correctly. It explains when to call it and what to expect, without over-explaining return values already covered by the 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?

The tool takes zero parameters, so there are no parameter semantics to describe. The baseline score for a parameterless tool is 4, and the description correctly adds no misleading 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 states a specific verb and resource: it returns server version and diagnostic info. It clearly distinguishes itself from all sibling Tesla data-query tools by being a build/deployment check.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

It explicitly says 'call first to confirm the build' and lists support, debugging, and deployment verification as use cases. It gives clear context but does not discuss when not to use it or name 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?

With no annotations, the description carries the full burden and does useful work: it discloses required QWEATHER_API_KEY and QWEATHER_API_HOST environment variables, says missing credentials return a friendly hint, and states that it changes nothing else. This effectively communicates read-only behavior and a failure mode, though it does not mention rate limits or permissions explicitly.

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 with the core purpose, then details the weather fields, relationship to outside_temp, and credential requirements. It is appropriately sized for a simple one-parameter tool, with only minor overhead such as the Chinese provider name.

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 optional parameter, 100% schema coverage, and an existing output schema, the description is largely complete: it explains what is fetched, where the location comes from, and what happens without API credentials. The main gap is stronger guidance on when to select it over related sibling tools.

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 only parameter, car_id, is fully described in the schema as a vehicle ID filter with a default from TESLA_CAR_ID or the first car. The description adds no additional parameter-level meaning beyond that schema description, so this is the baseline adequate score for high schema coverage.

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?

States a specific verb and resource: fetch current weather at the vehicle's latest GPS location, via QWeather. It also gives rich returned fields and notes that it complements TeslaMate's single outside_temp sensor, making its scope distinguishable from generic status or weather-efficiency 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?

Usage is implied by the description: use it when current, detailed weather at the car's location is needed, especially to supplement the simple outside_temp sensor. However, it does not explicitly say when to choose this over siblings such as tesla_efficiency_by_weather or tesla_location_history, and it provides no exclusions.

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