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Verifications list open

verifications_list_open
Read-onlyIdempotent

Seller side: list open verification jobs you may take as an independent verifier. A job whose acceptance block carries independent_verifier opens one once its deliverable passes every machine check. Lists only jobs that are not your store's, whose buyer does not own your store, in your settlement currency, and whose min_calibration_bp your standing_bp meets (your share of finished jobs released with no dispute the buyer won; null until your calibration reaches the minimum sample). Each carries the job's scope, output schema, verifier_class, the fee_pence it pays from the job's escrow (0 on a revision whose fee another verifier holds) and the claim deadline. The deliverable shows only after you claim.

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": {
      "limit": "<limit>"
    },
    "name": "verifications_list_open"
  }
}

Input Schema

NameRequiredDescriptionDefault
limitYesMost verifications to return.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.4/5.0
Behavior5/5

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

Beyond the read-only, idempotent annotations, it explains when a job opens, what fields each result carries, how fee_pence derives from escrow, the zero-fee revision nuance, and that the deliverable remains hidden until claim. This is substantial behavioral context for a list 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?

The purpose is front-loaded and every sentence adds relevant information. The eligibility sentence is long and nested, and phrases like 'min_calibration_bp your standing_bp meets' are dense, but there is little wasted wording.

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

Completeness5/5

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

For a complex eligibility-filtered list with no output schema, the description covers the key return fields, fee and deadline behavior, and deliverable visibility. The limit parameter is fully documented by the schema, leaving no major gap for an agent to call this correctly.

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?

The input schema has 100% description coverage for the single limit parameter, including default, minimum, and maximum. The description adds no additional meaning about limit, so the baseline of 3 is appropriate.

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 opens with a specific verb and resource: list open verification jobs a seller-side independent verifier may take. It clearly distinguishes this listing operation from sibling actions such as claiming or submitting a verdict.

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?

It gives detailed eligibility conditions for when a job appears: not your store's, buyer does not own your store, settlement currency match, and sufficient standing_bp against min_calibration_bp. It does not explicitly name alternatives or when-not-to-use cases, but the filtering rules provide strong usage context.

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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