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jdhart81

QuantaOptima

by jdhart81

quantaoptima_optimize

Solve black-box optimization with quantum-inspired selection and receive best solution, fitness metrics, and tamper-evident audit logs for each step.

Instructions

Run quantum-inspired optimization using the Measurement-Collapse Pruner.

Uses interference-enhanced selection to solve black-box optimization problems with built-in cryptographic auditing and interpretability telemetry.

Every optimization step is automatically logged to the audit chain.

FREE tier: sphere, rastrigin, rosenbrock | 10 dims | 100 iters PRO tier: all 6 objectives | 100 dims | 5000 iters

Args: objective: Built-in function name (sphere, rastrigin, rosenbrock, ackley, griewank, levy). The optimizer MAXIMIZES, so built-ins are negated. dimensions: Number of variables (2-100). bounds_low: Lower bound for all variables. bounds_high: Upper bound for all variables. max_iterations: Maximum iterations (10-5000). population_size: Candidate solutions per iteration (10-200). temperature: Boltzmann temperature. Higher = more exploration. seed: Random seed for reproducibility.

Returns: JSON with best_solution, best_fitness, quantum_metrics, and audit status.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
seedNo
objectiveNosphere
bounds_lowNo
dimensionsNo
bounds_highNo
temperatureNo
max_iterationsNo
population_sizeNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.4.0

TDQS

A4.4/5.0
Behavior4/5

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

No annotations are provided, so the description carries the full behavioral burden. It discloses that every step is logged to the audit chain, that built-ins are negated because the optimizer MAXIMIZES, and that cryptographic auditing/telemetry are included. It doesn't state runtime cost or whether tier limits cause errors vs. silent truncation, but the operational semantics are unusually well covered.

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?

Front-loads the purpose, then tiers, then args, then returns. The tier block and Args list are efficient; the marketing phrasing ('interference-enhanced', 'quantum-inspired') is a bit redundant but short. Overall well-structured and skimmable.

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?

Output schema exists so return values are covered, yet the description still names the return fields (best_solution, best_fitness, quantum_metrics, audit status). With no annotations, param coverage filled by the description, and tier constraints explained, an agent has everything needed to invoke correctly.

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 does: it documents all 8 parameters with valid ranges (dimensions 2-100, max_iterations 10-5000, population_size 10-200), objective names, MAXIMIZES/negation behavior, and temperature semantics (higher = more exploration). Ranges and semantics go beyond what the bare schema provides.

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?

States a specific verb-and-resource pair ('Run quantum-inspired optimization using the Measurement-Collapse Pruner' with interference-enhanced selection) plus an explicit scope of built-in objectives. The 'optimize' framing and audit-chain logging distinguish it from siblings like benchmark, observe, and explain.

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?

Clear context on when to use (black-box optimization with auditing) and a tier table indicating which objectives/dimensions/iters apply to FREE vs PRO. No explicit when-not-use guidance or named alternatives, but the tier constraints implicitly route the agent.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.