SiteCheck Company Records

US EPA fuel economy ratings from FuelEconomy.gov

gov_fueleconomy_gov

Read-only

US EPA fuel economy ratings from FuelEconomy.gov for a model year, make and model: each matching vehicle's EPA combined, city and highway MPG, fuel type, drive type and CO2 tailpipe grams per mile. Up to 3 vehicles per call. Price: $0.002 in USDC per successful call, paid with x402 (Base, Solana or Arc); failed calls are not charged.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "year": "<year>",
      "make": "<make>",
      "model": "<model>"
    },
    "name": "gov_fueleconomy_gov"
  }
}

Input Schema

Name	Required	Description
`make`	Yes	Make, e.g. Toyota
`year`	Yes	Model year, 4 digits, e.g. 2024
`model`	Yes	Model, e.g. Camry

Output Schema

Name	Required	Description	Default
`make`	No
`year`	No
`model`	No
`source`	No
`vehicles`	No
`checkedAt`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 23, 2026

TDQS

A3.8/5.0

Behavior4/5

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

Annotations only declare readOnlyHint and openWorldHint, so the description carries real extra weight and delivers: a hard result cap of 3 vehicles per call, a stated price ($0.002 USDC per successful call), the payment rails (x402 on Base, Solana or Arc), and the fact that failed calls are not charged. It stops short of explaining what happens on an ambiguous or zero-match make/model.

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?

Two tight sentences: the first front-loads what you get, the second front-loads the operational limits (cap and cost). No filler, and the cost/limit details come before an agent commits to the call.

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 an output schema, annotations and full param documentation already in place, the remaining burden is cost and cardinality, both of which are covered. A note on zero-match behavior or matching tolerance would make it complete.

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

Parameters3/5

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

Schema description coverage is 100% with all three params documented inline (including examples like Toyota, 2024, Camry), so the baseline is 3. The description restates the same year/make/model framing without adding matching semantics such as exact-match vs. fuzzy or case sensitivity.

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 names the specific resource (US EPA fuel economy ratings from FuelEconomy.gov) and the exact key (model year, make, model), then enumerates the returned fields (combined/city/highway MPG, fuel type, drive type, CO2 g/mi). No sibling tool covers fuel economy, so there is nothing concrete to disambiguate against, which keeps it just 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.

Usage Guidelines3/5

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

Usage is implied by the required year/make/model key rather than stated: an agent can infer this is a vehicle-spec lookup, but there is no explicit 'use this when...' guidance and no mention of alternatives (e.g. gov_energy_gov for broader energy data).

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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