MCP servercom.rqmtechnologies/waveengine

Validate a bounded request against the live catalog and return a non-binding price and duration estimate without authentication.

Return one full approved public Wave capability descriptor.

Return one realistic illustrative request/result shape, artifact list, expected duration, and price guard.

Retrieve one principal-scoped bounded Wave result artifact.

Read one completed and already-settled product result.

Read one authorized Wave or Studio managed-simulator job.

Retrieve the durable Account Core receipt for one settled WaveEngine reservation.

Return complete agent discovery contracts and readiness for the exact 20/20/20 RQM specialist work portfolio.

Describe the preserved v0/v1 surface or the dynamic v2/v3 Wave catalog.

Quote, settle, and submit one allowlisted WaveEngine, Studio, or Robotics buyer job using x402.

Execute the catalog-owned bounded example through the real typed WaveEngine adapter without accepting arbitrary input.

Pay anonymously with x402 by default, or explicitly use an authenticated Account Core balance, then submit one idempotent WaveEngine job.

Select RQM buyer jobs from work-language requests using deterministic semantic scoring over instructions, examples, outputs, and next steps.

Deterministically rank executable WaveEngine capabilities for a natural-language task and bounded filters.

Problem: Apply this bounded phase correction and verify the supplied preservation assertions. Input: JSON with sample rate hz, channels, angle radians, assertions. Result: correcte

Problem: Compare these two supported Wave pipelines on the supplied fixture and caller tolerances. Input: JSON with left pipeline, right pipeline, fixture, maximum rmse, maximum ab

Problem: Compare these two bounded waveforms against the supplied alignment and numeric rules. Input: JSON with sample rate hz, channels, reference channel id, candidate channel id

Problem: Compile this curated processing recipe and return its validated pipeline and execution trace. Input: JSON with fixture, maximum round trip rmse. Result: validated pipeline

Problem: Convert this bounded complex capture into a checksummed spectral artifact with declared normalization. Input: JSON with sample rate hz, channel, normalization. Result: spe

Problem: Diagnose this coordinated complex capture for bounded Wave-Intelligence degradation findings without inferring a physical cause. Input: JSON with sample rate hz, channels.

Problem: Fit a bounded quaternion NLMS equalizer to this received and desired pilot capture. Input: JSON with input channels, output channels, received, desired, pilot mask. Result

Problem: Convert this coordinated complex capture into the deterministic versioned Wave-Intelligence feature vector. Input: JSON with sample rate hz, channels. Result: versioned fe

Problem: Fit a bounded structured quaternion ridge model to these coupled channel features and targets. Input: JSON with feature count, output count, features, targets. Result: fit

Problem: Accept or reject this bounded signal capture against the caller-supplied metric rules and return every measurement and violation. Input: JSON with sample rate hz, channels

Problem: Generate this bounded sine or safe-formula signal fixture and return reproducibility, measurement, checksum, and validation evidence. Input: JSON with fixture. Result: sig

Problem: Measure phase drift in this bounded complex observation against the supplied reference and tolerances. Input: JSON with reference, observation, maximum absolute drift radi

Problem: Optimize this supported Wave pipeline against the supplied operation-count objective and equivalence rule. Input: JSON with operations, fixture, objective, maximum operati

Problem: Reconstruct this bounded capture under the supplied transfer assumption and verify it against the reference rules. Input: JSON with observation, reference, assumed transfe

Problem: Convert these bounded frequency bins into time-domain samples and verify the caller's round-trip rule. Input: JSON with sample rate hz, frequency bins, normalization, rule

Problem: Rotate these two coordinated complex channels with the supplied rotor and validate caller invariants. Input: JSON with channels, rotor, rules. Result: rotated channels and

Problem: Transform these Jones vectors or channel operators between the explicitly named SU(2) frames and verify invariants. Input: JSON with mode, rotor. Result: transformed vecto

Problem: Compare this reconstruction with the supplied reference and acceptance rules. Input: JSON with reference, reconstruction, rules. Result: reconstruction differences and rul

Problem: Accept or reject this bounded capture against the caller's supplied spectral criteria. Input: JSON with sample rate hz, channel, normalization, rules. Result: spectral met

Problem: Execute and verify this bounded Wave IR pipeline against the required representation, shape, and optional round-trip assertions. Input: JSON with sample rate hz, channels,