SiteCheck Trade & Energy

US truck freight demand and capacity proxies from FRED

logistics_dry_van_capacity

Read-only

US truck freight demand and capacity proxies from FRED (monthly): Cass Freight Index shipments and expenditures, the implied freight cost per shipment (expenditures / shipments), and ATA truck tonnage, each with month-over-month and year-over-year change. National, monthly data; it is not a lane-level tender rejection or load-board feed. Related tools on this server, for a different need: logistics_dry_port_inland_depot when you need Inland container depots and dry ports by country. Price: $0.002 in USDC per successful call, paid with x402 (Base, Solana or Arc); failed calls are not charged.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "logistics_dry_van_capacity"
  }
}

Input Schema

Name	Required	Description	Default
`months`	No	Months of history, 1 to 24 (default 6)

Output Schema

Name	Required	Description	Default
`source`	No
`history`	No
`reading`	No
`checkedAt`	No
`cassShipmentsIndex`	No
`ataTruckTonnageIndex`	No
`cassExpendituresIndex`	No
`impliedCostPerShipment`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 23, 2026

TDQS

A4.1/5.0

Behavior4/5

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

Annotations already declare readOnlyHint and openWorldHint, and the description adds meaningful context beyond them: the metered pricing model ($0.002 USDC per successful call via x402 on Base, Solana or Arc) and the useful guarantee that failed calls are not charged. It also discloses data frequency and geography, though not freshness/lag of the FRED series, which matters for a monthly indicator tool.

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?

Content is front-loaded: what the data is, then the negative scope constraint, then the related tool, then pricing. Most sentences earn their place, though the 'Related tools on this server, for a different need' phrasing is slightly clunky and the dry-port pointer is of marginal relevance to truck freight series.

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 present and annotations covering safety, the description does the remaining work: series composition, frequency, geography, scope exclusions, and cost model are all stated. The only real gap is data recency/lag for these monthly FRED series, which an agent scheduling calls would benefit from knowing.

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 coverage is 100% and the single 'months' parameter is documented in-schema with range and default. The description adds no further parameter meaning (e.g., how history length affects the MoM/YoY computations), so this sits at the baseline for fully-documented schemas.

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?

Names the resource precisely (US truck freight demand and capacity proxies from FRED) and enumerates the specific series included: Cass shipments and expenditures, implied cost per shipment, and ATA tonnage, each with MoM and YoY change. The negative scope statement (not lane-level tender rejection or load-board data) separates it from logistics_truck_loadboard_rate and logistics_spot_freight_rates.

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?

Gives clear context for selection: national, monthly aggregate freight demand rather than lane-level rates, which implicitly routes lane-level needs to the load-board siblings. It also explicitly names a related tool (logistics_dry_port_inland_depot) so the agent knows what not to call this for. It stops short of an explicit 'use this when you need X' routing sentence, and the named alternative is a weak match for the nearest real alternatives.

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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