Quantum Expectations

Historic Hardware Series (Fidelity or Qubit Count)

get_historic_series

Read-onlyIdempotent

Return the full historic time series — either two-qubit gate error rates ("fidelity") or physical qubit counts ("qubit-count") — broken down by hardware type. Each datapoint carries a source URL. Use this to extrapolate trends — "when might hardware reach X?" — or pair with fit_historic_series for a log-linear fit on one hardware type.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`seriesType`	Yes	"fidelity" → 2-qubit gate error rates; "qubit-count" → physical qubit counts.

TDQS

A4.5/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds behavioral context by noting the data is 'full' and 'broken down by hardware type,' and that each datapoint carries a source URL, which helps the agent understand the return structure beyond the schema.

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 two sentences, front-loaded with the main verb and resource, then followed by concrete use cases and a pairing suggestion. Every clause adds value without unnecessary fluff, making it well-structured and appropriately sized for the tool's complexity.

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 one parameter, comprehensive annotations, and no output schema, the description covers all essential aspects: what the tool does, what each datapoint contains (source URL), and how it can be used in combination with fit_historic_series. The tool is simple enough that this description is fully complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema has 100% coverage for the only parameter (seriesType), including a description of each enum value. The description repeats the meaning of 'fidelity' and 'qubit-count' but adds no new parameter-level information beyond what the schema already provides. Baseline 3 is appropriate.

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 clearly states the tool returns a full historic time series of either two-qubit gate error rates or qubit counts, broken down by hardware type. The specific verb 'Return' and explicit resource scope distinguish it from sibling tools like fit_historic_series, which fits curves rather than retrieving raw data.

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?

Explicit guidance is provided: 'Use this to extrapolate trends — "when might hardware reach X?" — or pair with fit_historic_series for a log-linear fit on one hardware type.' This clearly states when to use the tool and suggests an alternative/companion tool, fulfilling the when-to-use vs alternatives requirement.

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

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TDQS

A4.4/5.0

Disambiguation5/5

Every tool has a clearly distinct role: compute_expectation and compute_required_error_rate are forward/inverse pairs, compare_hardware_scenarios is a batch wrapper for compute_expectation, and the list_* and get_* tools each target a different data category (hardware, timing, historic, examples, codes). The fit_historic_series tool is distinct from get_historic_series (analysis vs. retrieval). No two tools appear ambiguous.

Naming Consistency5/5

All tool names follow a strict lower_snake_case verb_noun pattern: compute_*, get_*, list_*, fit_*, compare_*. The verbs precisely indicate the action (computation, retrieval, fitting, comparison) and are used consistently across the set.

Tool Count5/5

With 12 tools, the server is well within the ideal 3-15 range. Each tool serves a distinct purpose in the quantum resource estimation workflow: data listing, forward/inverse expectation calculations, fault-tolerant resource estimation, rate computation, historic analysis, and context retrieval. No tool feels redundant or unnecessary.

Completeness5/5

The tool surface comprehensively covers the domain: current hardware data (list_current_quantum_computers, list_hardware_timings), historical trends (get_historic_series, fit_historic_series), algorithm resource examples (list_example_algorithms), error correction codes (list_qldpc_codes), forward and inverse error calculations (compute_expectation, compute_required_error_rate), batch comparison (compare_hardware_scenarios), deep resource estimation (compute_fault_tolerant_resources), a throughput metric (compute_quantum_volume_rate), and the agent brief. There are no obvious missing operations for the stated purpose.

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