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Privi Quantum

by io.github.canoydario847-cloud
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Quantum optimize

quantum_optimize

Solve a factory decision with the Quantum Approximate Optimization Algorithm. Returns the decision in plain terms, how close it is to the proven optimum, and a post-quantum signed attestation (Ed25519 + ML-DSA-87) that is anchored on chain (Base). Paid per call via x402 when payments are on (see quantum_pricing).

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": {
      "problem": "<problem>",
      "data": "<data>"
    },
    "name": "quantum_optimize"
  }
}

Input Schema

NameRequiredDescriptionDefault
dataYesProblem data in the shape quantum_problems describes
shotsNoMeasurements to sample from the trained circuit (1-100000)
layersNoQAOA depth p (1-4). Deeper finds better answers more often but runs longer.
problemYesbalance_lines | select_lots | split_conflicts | qubo (see quantum_problems)
include_qasmNoAlso return the trained circuit as OpenQASM 2.0 for real quantum hardware

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.7/5.0
Behavior4/5

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

Annotations already declare non-read-only, open-world, non-idempotent behavior, so the bar is lower; the description still adds real value by disclosing that calls are paid via x402 when payments are enabled and that results carry an on-chain-anchored Ed25519 + ML-DSA-87 attestation. What's missing is cost magnitude and whether repeated calls with the same input consume budget each time.

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?

Three tight sentences lead with the action and result, then append the payment caveat with a pointer rather than explaining pricing inline. Dense but every sentence carries information; the parenthetical crypto detail (Ed25519 + ML-DSA-87) is arguably more than an agent needs to select the tool.

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 no output schema, the description correctly spends its words describing the return shape (plain-terms decision, approximation to optimum, attestation), which is exactly what an agent can't get elsewhere. Given the tool is non-idempotent and paid, a sentence on cost or on what the attestation enables would close the remaining 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 100%, so all five parameters (including shots, layers, include_qasm, and the problem/data pair) are already fully documented in the schema. The description adds no parameter-level meaning beyond pointing at quantum_pricing and quantum_problems, which the schema also does, so baseline 3 applies.

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 names a specific verb and resource: solving a factory decision with QAOA, plus the shape of the result (decision, gap to optimum, signed attestation). It implicitly separates itself from low-level siblings like quantum_run_circuit by emphasizing the packaged decision + attestation output, but it never names a sibling explicitly, so the differentiation is inferential rather than stated.

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?

It gives useful situational context (payments on/off via x402, see quantum_pricing) but never states when to pick this over quantum_run_circuit, quantum_run_hardware, or quantum_advantage_receipt. The agent must infer that this is the high-level solver path from the returned-value description alone.

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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