MCP servercom.rqmtechnologies/jobs

Request cancellation of one authorized product job.

Create an idempotent Stripe Checkout URL for human prepaid funding.

Read the authenticated canonical Account Core prepaid USD balance.

Read this agent's saved job, result and receipt. Never authorizes another purchase.

Return one full approved public Wave capability descriptor.

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.

List up to 100 recent jobs or recover the original purchase using its idempotency key.

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.

Return a preserved v0 placeholder or a durable v1 Account Core USD quote.

Problem: Analyze these bounded force and disturbance inputs against the supplied response model and criteria. Input: JSON with mass kg, damping n s per m, stiffness n per m, time s

Problem: Assess this bounded calibration dataset against the supplied residual, coverage, and consistency rules. Input: JSON with length unit, observations, rules. Result: pass, fa

Problem: Check this bounded geometry and trajectory against the supplied collision and clearance constraints. Input: JSON with trajectory, obstacles, robot radius, minimum clearanc

Problem: Compare these bounded controller responses on identical traces using the supplied objective rules. Input: JSON with baseline, candidate, rules. Result: metric values, thre

Problem: Compare digital-twin output with mapped observations against caller tolerances. Input: JSON with predicted, observed, declared mapping, rules. Result: typed verdict, measu

Problem: Compare planned and observed trajectories against caller tolerances. Input: JSON with planned, observed, rules. Result: typed verdict, measured metrics, candidate only whe

Problem: Compare these bounded estimator outputs against the supplied reference and objective rules. Input: JSON with reference, estimators, rules. Result: per-estimator metrics, r

Problem: Compute a bounded actuator alignment correction from these observations and caller acceptance limits. Input: JSON with observations, model, rules. Result: correction param

Problem: Diagnose this bounded trajectory for the supplied oscillation, divergence, and stability indicators. Input: JSON with frame id, length unit, samples, rules. Result: pass,

Problem: Fuse these bounded synchronized sensor observations under the supplied diagonal uncertainty model and rules. Input: JSON with observations, model, rules. Result: fused sta

Problem: Validate this bounded frame graph and report unit, axis, handedness, rotation, and transform-direction violations. Input: JSON with frames, declared length unit, declared

Problem: Convert these bounded sensor observations into the supplied target frame and unit convention. Input: JSON with frame graph, target frame id, observations. Result: normaliz

Problem: Tune supported control parameters against this bounded disturbance model and supplied response objectives. Input: JSON with mass, damping, disturbance, time step seconds,

Problem: Optimize a bounded trajectory only when speed and duration constraints pass. Input: JSON with trajectory, maximum speed, maximum duration s. Result: typed verdict, measure

Problem: Reconstruct this bounded trajectory under the supplied smoothing model and residual rule. Input: JSON with samples, model, rules. Result: smoothed trajectory, residual RMS

Problem: Replay this bounded workcell trace against the supplied event assertions. Input: JSON with model id, events, assertions. Result: timeline, assertion results and trace dige

Problem: Tune a supported controller only within supplied bounds and safety limits. Input: JSON with supported controller, current gain, fixture error, target error.... Result: typ

Problem: Accept or reject this bounded closed-loop model under the supplied stability criteria. Input: JSON with discrete state matrix, criteria. Result: eigenvalues, spectral radi

Problem: Accept or reject a bounded trajectory against caller kinematic limits. Input: JSON with trajectory, maximum speed, maximum acceleration. Result: typed verdict, measured me

Problem: Validate timestamps, ordering, rates, gaps, and synchronization in this bounded trajectory or sensor trace. Input: JSON with clock id, samples, rules. Result: pass or fail

Submit one catalog job within the account owner's saved spending policy. Reuse idempotency_key on every retry. This spends prepaid credits; it never signs x402 or funds the account

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

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

Server-side Jobs-rail x402 settle for connector hosts that cannot sign. Replays the same idempotency key with PAYMENT-SIGNATURE. An RQM-owned operator signer is not external revenu

Reserve an exact quote and submit one bounded managed-simulator job.

Reserve an exact quote and submit one bounded catalog-approved Wave job.