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Novarithm Agent Hub: trust and commerce checks

by com.novarithm.www
OverviewDiscussionsAdmin

renewal_cohort_check

[PAID $0.006 USDC/call, x402, eip155:8453] Compute UTC monthly cohorts and one declared renewal cycle per supplied subscription. Eligible at supplied due date plus grace; a matching confirmation is a declaration, never proof of collection. Up to 1000 subscriptions and 1000 events. No revenue, MRR, ARR or certain churn inferred. 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": {
      "as_of": "<as_of>",
      "subscriptions": "<subscriptions>",
      "events": "<events>"
    },
    "name": "renewal_cohort_check"
  }
}

Input Schema

NameRequiredDescriptionDefault
as_ofYes
eventsYes
grace_secondsNo
subscriptionsYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A3.8/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does a lot: it discloses paid x402 pricing, authentication limits, input caps, body-size limits, and important inference caveats such as 'never proof of collection' and 'No revenue, MRR, ARR or certain churn inferred.' It stops short of describing the return shape or deterministic/idempotent behavior.

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 description is dense but appropriately sized for a complex paid computation tool. The payment notice and purpose are front-loaded, though multiple caveats are packed into one paragraph rather than grouped.

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?

Given four parameters, nested event objects, no annotations, and no output schema, the description covers operational constraints and caveats well. However, it does not explain what the computation returns or how the output should be interpreted, which leaves a meaningful gap.

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 0%, so the description must compensate. It adds useful meaning for grace_seconds and the due-date eligibility logic, plus the array caps, but it does not explain as_of, cohort_at, occurred_at, event_id, or the four event kinds.

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?

The description states a specific verb and resource: compute UTC monthly cohorts and one declared renewal cycle per supplied subscription. It is immediately distinguishable from the sibling tools, none of which deal with subscription renewal cohorts.

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 subscription and event inputs, but there is no explicit when-to-use, when-not-to-use, or alternative-tool guidance. The caveats about eligibility and declarations guide interpretation but do not route the agent between tools.

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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