toyota-mcp

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Driving statistics over a period

toyota_get_trip_summary

Read-onlyIdempotent

Retrieve aggregated driving statistics for a calendar period or rolling window, including average consumption, EV-mode share, and total distance. Use to monitor trends by day, week, month, or year.

Instructions

Aggregated driving statistics over a rolling window or a calendar period.

Use for: average consumption over the last 7 days, EV-mode share this month, total distance this year. Consumption is recomputed over the whole window (total fuel vs total distance), not a mean of daily means. Pass period to match the app's calendar figures.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`days`	No	Calendar days to cover, ending today (inclusive).
`period`	No	Calendar period as shown in the MyToyota app (today, this_week, this_month, this_year); overrides days.

Output Schema

TableJSON Schema

Name	Required	Description
`note`	No	Set when the window contains no trips.
`period`	No	Set when the window is a calendar period (as in the app).
`countries`	Yes	ISO 3166-1 alpha-2 codes of countries driven in.
`freshness`	Yes
`window_to`	Yes
`ev_distance`	Yes
`window_from`	Yes
`average_speed`	No
`fuel_consumed`	Yes
`total_distance`	Yes
`ev_ratio_percent`	No	Share of the distance driven in electric mode.
`days_with_driving`	Yes
`ev_duration_hours`	No
`average_consumption`	No	Recomputed over the whole window (total fuel vs total distance).
`total_duration_hours`	Yes
`ev_time_ratio_percent`	No	Share of the driving time spent in electric mode.

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Sep 6, 2026

TDQS

A4.5/5.0

Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint. The description adds meaningful behavioral context by explaining that consumption is recomputed over the whole window as total fuel vs total distance, not as a mean of daily means. This clarifies a subtle computation behavior beyond the annotations.

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

Conciseness5/5

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

The description is concise, front-loaded with the tool's purpose, and uses brief examples to clarify usage. Every sentence contributes meaningfully without unnecessary detail.

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

Completeness5/5

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

With two optional parameters, full schema coverage, an output schema, and read-only/idempotent annotations, the description provides all essential context. It covers both parameter modes, the computation nuance, and representative use cases, leaving no critical gaps for an agent to select and invoke the tool correctly.

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

Parameters4/5

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

Schema description coverage is 100%, so the baseline is 3. The description adds usage-oriented semantics by mapping examples to parameters, such as 'last 7 days' to days and 'this month'/'this year' to period, and recommending period to match the app's calendar figures.

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

Purpose5/5

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

The description clearly states the tool returns aggregated driving statistics over a rolling window or calendar period, with concrete use cases such as average consumption over the last 7 days. It differentiates this from raw trip-level tools by emphasizing aggregation and windowed calculations.

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

Usage Guidelines4/5

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

The description provides explicit use cases ('Use for: average consumption over the last 7 days, EV-mode share this month, total distance this year') and explains when to use the period parameter. It does not name alternatives or explicitly state when not to use this tool, so it misses the full exclusion guidance.

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