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ביטוחון: Israeli car data and car-insurance facts

Model facts / נתוני דגם

model_info
Read-onlyIdempotent

Use for questions about a car model (optionally one model year) without a plate: cars on the road in Israel, trims, engines, share of cars with each safety system, importer list price when new, recall notices, the annual licence fee per model year (per trim with a fuel-use estimate when a year is given), and Israel Police theft counts 2023-2025 with a rate per 1,000 cars. Facts, not a ranking. נתוני דגם: כמה על הכביש, גרסאות, מערכות בטיחות, מחירון כשהיה חדש, ריקולים, אגרת רישוי וגניבות.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
makeYesMake in Hebrew, English or slug, e.g. "קיה", "Kia", "kia". / יצרן.
yearNoModel year (שנת ייצור). Optional.
modelYesModel in Hebrew, English or slug, e.g. "פיקנטו", "Picanto", "picanto". / דגם.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
errorNo
modelNo
sourceNo
handoffNoHow the person can ask the operator for a quote (offer only when relevant; never automatic).
summaryNo
data_dateNoYYYY-MM-DD the data is correct to.
disclaimerYesHebrew disclaimer; relay it.
model_yearNo
citation_urlNoPage to cite (link it when you use the facts).
disclaimer_enNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.8/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, and openWorldHint=false, so the safety profile is covered. Beyond that, the description adds real context: the domain is Israel, results are "facts, not a ranking," and the year parameter changes output granularity (per-trim fee with fuel-use estimate). It does not cover failure modes (unknown model) or match leniency.

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

Conciseness4/5

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

The purpose clause is front-loaded before the data enumeration, which is the right ordering. The long comma-separated content list borders on run-on, and the trailing Hebrew sentence is largely a restatement of the same content for bilingual users, adding length without new selection guidance.

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

Completeness4/5

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

With annotations and an output schema present, the description's job is mainly scope, and it enumerates every data area (on-road counts, trims, engines, safety-system shares, list price, recalls, licence fee, theft counts). It remains thin on how results differ from overlapping siblings and on empty/not-found behavior.

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 coverage is 100% with per-parameter examples (Hebrew, English, slug), so the schema carries the basics. The description still adds meaning the schema does not: the year parameter is explicitly optional and, when supplied, yields per-trim licence fee plus a fuel-use estimate, which tells the agent why to pass it.

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

Purpose4/5

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

The description gives a precise subject: facts about a car model identified by make/model (optionally year), explicitly excluding plate-based lookup ("without a plate"), which separates it from lookup_vehicle. However, it does not differentiate itself from overlapping siblings such as search_models, theft_by_model, or explain_licence_fee, even though it returns theft counts and licence-fee data.

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

Usage Guidelines3/5

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

"Use for questions about a car model ... without a plate" is a usable when-to-use condition, and "optionally one model year" signals a mode. But there is no explicit exclusion or routing against the several siblings whose data this tool partly duplicates (theft_by_model, explain_licence_fee, search_models), leaving the agent to infer the split.

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

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