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Quantum Expectations

by com.quantum-expectations

Compute Quantum Expectation

compute_expectation
Read-onlyIdempotent

Given a quantum circuit (2-qubit error rate p, qubit count n, depth d), compute the effective error rate, success probability, and optional surface-code or qLDPC overhead. The response is self-describing (formulas, assumptions, caveats, glossary, SOTA hardware, historic series with source URLs) so an agent can reason from one call. For the inverse ("what hardware do I need?") use compute_required_error_rate; to rank multiple platforms in one call use compare_hardware_scenarios.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
verboseNoWhen false, omits hardwareContext, formulas, assumptions, caveats, glossary, examples, and exampleProblems from the response — leaving only modelVersion, scenario, and result. Use for parameter sweeps where that context would repeat unchanged.
compDepthYes
numQubitsYes
hardwareIdNoAlias for qubitErrorRate: resolves to the 2-qubit error rate of the given SOTA hardware entry from list_current_quantum_computers. Supply exactly one of qubitErrorRate or hardwareId.
qubitErrorRateNoPer-gate 2-qubit error rate p, in (0, 0.1]. Supply exactly one of qubitErrorRate or hardwareId.
useErrorCorrectionNo
distanceSurfaceCodeNoSurface code distance: odd integer in [3, 31]. Required when useErrorCorrection=true and the surface code is selected; ignored when a qLDPC code is selected.
errorCorrectionCodeNoEither "surface" (default when useErrorCorrection=true) or a qLDPC code id from list_qldpc_codes. "surface-code" is accepted as an alias for "surface".

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed4 schema fields changed
    • addedInput schema / properties / distanceSurfaceCode / description
      Added value: +"Surface code distance: odd integer in [3, 31]. Required when useErrorCorrection=true and the surface code is selected; ignored when a qLDPC code is selected."
    • removedInput schema / properties / distanceSurfaceCode / maximum
      Removed value: -31
    • removedInput schema / properties / distanceSurfaceCode / minimum
      Removed value: -3
    • changedInput schema / properties / distanceSurfaceCode / type
      Previous value: -"integer"New value: +"number"
  2. Changed1 schema field changed
    • changedInput schema / $schema
      Previous value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"
  3. Changed1 schema field changed
    • addedInput schema / properties / errorCorrectionCode / description
      Added value: +"Either \"surface\" (default when useErrorCorrection=true) or a qLDPC code id from list_qldpc_codes. \"surface-code\" is accepted as an alias for \"surface\"."
  4. First observed

TDQS

A4.9/5.0
Behavior5/5

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

Annotations already establish read-only, idempotent, non-destructive behavior. The description adds significant value beyond those by disclosing that the response is self-describing with formulas, assumptions, caveats, glossary, SOTA hardware references, and source URLs, which lets an agent reason from one call without additional lookups. No contradiction with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is compact: two sentences that front-load the primary function, then add response characteristics and alternative routing. Every clause contributes useful information without redundancy.

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?

With no output schema, the description appropriately communicates what the response will contain: effective error rate, success probability, optional overhead, and a self-describing context. It also covers the main routing questions an agent would have, while remaining details such as verbose behavior and hardwareId aliases are already well documented in the input schema.

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 coverage is 63%, and the description adds useful mathematical framing by naming the key variables (2-qubit error rate p, qubit count n, depth d) and mentioning optional surface-code/qLDPC overhead. It partially compensates for the undocumented required numQubits and compDepth parameters, although it does not fully explain the error-correction parameter relationships.

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?

The description states a specific verb and resource: compute effective error rate, success probability, and optional surface-code or qLDPC overhead from a quantum circuit. It also explicitly differentiates from sibling tools by pointing to compute_required_error_rate for the inverse and compare_hardware_scenarios for ranking multiple platforms.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives explicit routing guidance: use this tool for forward calculation of error rates/success probabilities, use compute_required_error_rate when the question is inverted, and use compare_hardware_scenarios when ranking multiple platforms. This is clear when-to-use and when-not-to-use information.

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

All Quantum Expectations toolsInspect
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TDQS

A4.5/5.0
Disambiguation5/5

Every tool has a clearly distinct role: forward expectation, inverse required error rate, multi-hardware comparison, fault-tolerant resource estimation, quantum volume rate, plus separate list/get/fit tools for hardware, historic data, algorithms, timings, and qLDPC codes. Cross-references in descriptions make selection unambiguous.

Naming Consistency5/5

Tool names follow a predictable verb_noun pattern: compute_* for estimation operations, list_* for catalogs, get_* for retrieving specific data, plus compare_hardware_scenarios and fit_historic_series as clear descriptive exceptions. The naming style is consistent and readable.

Tool Count5/5

Twelve tools is well-scoped for a quantum computing estimation and hardware comparison server. Each tool covers a distinct capability without redundancy, and the count feels appropriate for the domain's breadth.

Completeness5/5

The tool surface covers forward computation, inverse computation, multi-hardware comparison, fault-tolerant resource estimation, historical trends, current hardware catalogs, algorithm examples, hardware timings, and error-correction code data. There are no obvious dead ends; tools compose naturally to answer 'what hardware do I need and when will it exist?'.

Resources