RQM Jobs MCP

Request cancellation of one authorized product job.

Create an idempotent Stripe Checkout URL for human prepaid funding.

Free, anonymous API-owned scientific capability contracts and domain suggestions. Start here before choosing or funding a quantum job. Inspect separate source, installed, hosted, simulator, live-QPU and public coverage; unknown means unverified. Read schemas, examples and limits; unavailable chemistry, cryptography or hybrid workflows must not be replaced with Bell or promised as execution. This finite registry is not the complete roadmap. Workflow tool references do not promise availability on this endpoint or eligibility for the caller. No account, balance, job, private policy, quote, reservation or hardware execution is accessed. Existing authenticated tools own those operations.

Read the authenticated canonical Account Core prepaid USD balance.

Read this agent's saved account-funded job by its returned job ID to check status and recover available result and receipt. Use after submission or when following up on an existing purchase; acceptance is not completion. Report the saved state and only available results, with their limitations. If the job ID was lost, use list_account_jobs with the original idempotency key first. 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 for this agent, or find the original account-funded purchase using its idempotency key. Use for purchase history and recovery before retrying an uncertain submission; use list_buyer_jobs or search_buyer_jobs for available services. Omit the key for recent history, or pass the exact original key to recover one purchase. Use a returned job ID with get_account_job for saved status, result and receipt. An empty response does not prove that a timed-out submission cannot still be accepted; preserve the original key and inputs on any retry. This read never purchases or reruns a job.

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 step seconds.... Result: modeled response, metrics and rule results. Limits: Software/model evidence only; 65536 request bytes; 5 s execution.

Problem: Assess this bounded calibration dataset against the supplied residual, coverage, and consistency rules. Input: JSON with length unit, observations, rules. Result: pass, fail, or indeterminate verdict, residual metrics, per-rule evaluations. Limits: Software/model evidence only; 65536 request bytes; 5 s execution.

Problem: Check this bounded geometry and trajectory against the supplied collision and clearance constraints. Input: JSON with trajectory, obstacles, robot radius, minimum clearance. Result: clearance, collision candidates and constraint results. Limits: Software/model evidence only; 65536 request bytes; 5 s execution.

Problem: Compare these bounded controller responses on identical traces using the supplied objective rules. Input: JSON with baseline, candidate, rules. Result: metric values, threshold verdicts, difference series. Limits: Software/model evidence only; 65536 request bytes; 5 s execution.

Problem: Compare digital-twin output with mapped observations against caller tolerances. Input: JSON with predicted, observed, declared mapping, rules. Result: typed verdict, measured metrics, candidate only when verified. Limits: Software/model evidence only; 65536 request bytes; 5 s execution.

Problem: Compare planned and observed trajectories against caller tolerances. Input: JSON with planned, observed, rules. Result: typed verdict, measured metrics, candidate only when verified. Limits: Software/model evidence only; 65536 request bytes; 5 s execution.

Problem: Compare these bounded estimator outputs against the supplied reference and objective rules. Input: JSON with reference, estimators, rules. Result: per-estimator metrics, rule verdicts, deterministic selected estimator or.... Limits: Software/model evidence only; 65536 request bytes; 5 s execution.

Problem: Compute a bounded actuator alignment correction from these observations and caller acceptance limits. Input: JSON with observations, model, rules. Result: correction parameters, pre/post residuals, applicability verdict. Limits: Software/model evidence only; 65536 request bytes; 5 s execution.

Problem: Diagnose this bounded trajectory for the supplied oscillation, divergence, and stability indicators. Input: JSON with frame id, length unit, samples, rules. Result: pass, fail, or unsupported verdict, indicator series, violation windows. Limits: Software/model evidence only; 65536 request bytes; 5 s execution.

Problem: Fuse these bounded synchronized sensor observations under the supplied diagonal uncertainty model and rules. Input: JSON with observations, model, rules. Result: fused state, diagonal covariance, sensor residuals and verdict. Limits: Software/model evidence only; 65536 request bytes; 5 s execution.

Problem: Validate this bounded frame graph and report unit, axis, handedness, rotation, and transform-direction violations. Input: JSON with frames, declared length unit, declared transform direction, declared.... Result: pass, fail, or unsupported verdict, exact findings, normalized evidence. Limits: Software/model evidence only; 65536 request bytes; 5 s execution.

Problem: Convert these bounded sensor observations into the supplied target frame and unit convention. Input: JSON with frame graph, target frame id, observations. Result: normalized or unsupported verdict, normalized observations, transform paths. Limits: Software/model evidence only; 65536 request bytes; 5 s execution.

Problem: Tune supported control parameters against this bounded disturbance model and supplied response objectives. Input: JSON with mass, damping, disturbance, time step seconds, baseline kp, baseline.... Result: candidate parameters, comparative metrics and constraint results. Limits: Software/model evidence only; 65536 request bytes; 5 s execution.

Problem: Optimize a bounded trajectory only when speed and duration constraints pass. Input: JSON with trajectory, maximum speed, maximum duration s. Result: typed verdict, measured metrics, candidate only when verified. Limits: Software/model evidence only; 65536 request bytes; 5 s execution.

Problem: Reconstruct this bounded trajectory under the supplied smoothing model and residual rule. Input: JSON with samples, model, rules. Result: smoothed trajectory, residual RMSE, caller-rule verdict. Limits: Software/model evidence only; 65536 request bytes; 5 s execution.

Problem: Replay this bounded workcell trace against the supplied event assertions. Input: JSON with model id, events, assertions. Result: timeline, assertion results and trace digest. Limits: Software/model evidence only; 65536 request bytes; 5 s execution.

Problem: Tune a supported controller only within supplied bounds and safety limits. Input: JSON with supported controller, current gain, fixture error, target error.... Result: typed verdict, measured metrics, candidate only when verified. Limits: Software/model evidence only; 65536 request bytes; 5 s execution.

Problem: Accept or reject this bounded closed-loop model under the supplied stability criteria. Input: JSON with discrete state matrix, criteria. Result: eigenvalues, spectral radius and rule results. Limits: Software/model evidence only; 65536 request bytes; 5 s execution.

Problem: Accept or reject a bounded trajectory against caller kinematic limits. Input: JSON with trajectory, maximum speed, maximum acceleration. Result: typed verdict, measured metrics, candidate only when verified. Limits: Software/model evidence only; 65536 request bytes; 5 s execution.

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 verdict, normalized timeline, exact timing defects. Limits: Software/model evidence only; 65536 request bytes; 5 s execution.

Purchase one discovered service using prepaid credits within the account owner's saved spending policy. First search_buyer_jobs for the task and inspect the selected request_schema, required inputs, limits, examples and evidence boundaries. Use its buyer service_id as capability_id; validate the user's inputs and ask for missing data rather than substituting an example. Set max_total_price to the caller-approved USD ceiling; discovery is not a binding quote and balance alone is not policy authorization. Acceptance or a queued job is not completion: retain the returned job ID and use get_account_job to read saved status, result and receipt. If submission times out or its outcome is uncertain, first recover with list_account_jobs using the original idempotency_key. Any retry must preserve the same key, capability, request and price ceiling; never create a new purchase or switch payment rails to recover. This tool never signs x402 or funds the account. Report only observed completion and findings within the service's evidence boundaries.

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 revenue.

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

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