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Novarithm Agent Hub: trust and commerce checks

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demand_lot_match

[PAID $0.005 USDC/call, x402, eip155:8453] Compare up to 100 supplied demand lines with 1000 supplied lots, at most 5000 same-item comparisons. Exact decimal quantity, same declared item ID/unit and exact required attributes; missing attributes/availability remain unknown. Lines are independent: no joint allocation, conversion, substitution, reservation or order. EOA EIP-712 payments only; ERC-1271/6492 unsupported. MCP body max 64 KiB. Experimental price; input facts are not authenticated.

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": {
      "demand": "<demand>",
      "lots": "<lots>"
    },
    "name": "demand_lot_match"
  }
}

Input Schema

NameRequiredDescriptionDefault
lotsYes
demandYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.1/5.0
Behavior5/5

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

With no annotations provided, the description carries the full burden and does so well: it discloses paid x402 access, chain, price, EOA-only payment support, unsupported signature schemes, body size limit, matching semantics, unknown-data behavior, and unauthenticated input facts. These are consequential operational details beyond the schema.

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 dense but front-loaded: payment/access details appear first, followed by the comparison purpose, matching rules, and limitations. Every sentence contributes operational or semantic information without filler.

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?

For a complex matching tool with no output schema and no annotations, the description is strong on inputs, limits, payment requirements, and matching behavior. The main remaining gap is that it does not describe the response shape or what a match result contains.

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 0%, so the description must compensate, and it substantially does: it defines demand-line and lot limits, exact decimal quantity behavior, same declared item ID/unit matching, required-attribute matching, and how missing availability/attributes are treated. It does not fully explain the roles of line_id/lot_id or the structure of the attributes objects.

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 specific verb and resource: compare supplied demand lines with supplied lots, including caps and matching criteria. It is clear what the tool does, but it does not explicitly differentiate itself from nearby sibling tools such as availability_intersection or price_availability.

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 matching description and the stated exclusions ('no joint allocation, conversion, substitution, reservation or order'), but there is no explicit when-to-use guidance or direct routing to alternatives. An agent can infer the use case, but not from stated 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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