ExchangeRebel — pay-per-call APIs for AI agents (x402 on Solana)

composite_inventory_lookup

$0.01 USDC on Solana per lookup - Your payment includes one composite lookup for the topic you submit: one answer section per requested leg (provider, class H1, licence, attribution, that leg's freshness, and its load state printed on every section), the matching entries from each loaded leg, the per-leg factors, and the A1 absence statement. Legs are never merged and no composite score or merged ranking is included. It does not include any agent-issued search-query data (class A1 — none is published by anyone), any Radar API data (barred, CC BY-NC), any verbatim WildChat prompts, or retention of your submitted topic (it is discarded; only its hash is kept).

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": {
      "topic": "<topic>"
    },
    "name": "composite_inventory_lookup"
  }
}

Input Schema

Name	Required	Description
`legs`	No	Leg ids to include; defaults to all cleared legs.
`topic`	Yes	The topic to look up in each requested leg.
`question_class`	No	Optional question class for the lookup.

Schema Changelog

Changes observed during successful MCP inspections.

AddedNov 4, 2026

TDQS

A3.9/5.0

Behavior5/5

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

With no annotations supplied, the description discloses the price and payment network, per-payment scope, section-level freshness and load state, non-merging behavior, prohibited data sources, and the A1 absence statement. It also gives an unusually specific privacy guarantee: the submitted topic is discarded and only its hash is retained.

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

Conciseness3/5

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

The sentences contain substantive information with little repetition, but the opening sentence is heavily overloaded with parenthetical output details. Dense jargon such as class H1, class A1, legs, and factors reduces scanability, and leading with pricing puts the core operation after the cost.

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

Completeness3/5

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

For a tool without annotations or an output schema, it covers compensation, result shape, exclusions, and data retention well enough for a basic topic-only invocation. It remains incomplete on the inventory's domain, available legs and question classes, and the operational relationship to its precheck and criteria siblings.

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%, so the baseline is 3. The description reinforces how topic and requested legs affect the result, but adds no valid leg identifiers, question_class values, or guidance on when supplying question_class changes the lookup.

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 identifies a paid composite lookup for a submitted topic and details its per-leg answer sections, matching entries, factors, and A1 absence statement. It does not explain what the inventory or legs represent, nor distinguish this lookup from the composite_inventory_precheck and composite_inventory_criteria siblings.

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 per-leg output establishes the intended use, while explicit exclusions such as no composite score, merged ranking, Radar API data, or verbatim WildChat prompts provide useful when-not guidance. It does not name an alternative for those needs or explain when to run the precheck or criteria tools first.

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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