Quantum Expectations

Fit Historic Series (log-linear)

fit_historic_series

Read-onlyIdempotent

Fit a log-linear trend (ln(value) = slope * year + intercept) to one historic series — fidelity or qubit-count — for one hardware type. Atomic primitive: compose with list_current_quantum_computers, compute_required_error_rate, or your own modelling to answer "when might hardware reach X?". residualStdDev is the BIASED (maximum-likelihood) RMS — divides by n, not (n - 2); on small series (n ≈ 3–5) inflate by √(n / (n - 2)) before building confidence intervals.

Input Schema

TableJSON Schema

Name	Required	Description
`seriesType`	Yes	Which historic series to fit: "fidelity" (2-qubit gate error rate) or "qubit-count" (physical qubits).
`targetValue`	No	Optional. When supplied, the response includes yearAtTargetValue — the extrapolated year the fit crosses this value (error rate for fidelity series, qubit count for qubit-count series). Null if slope is flat.
`hardwareType`	Yes	Hardware platform as used by get_historic_series.

TDQS

A3.9/5.0

Behavior4/5

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

Annotations declare safe read-only, idempotent operation, and the description adds important nuance about residualStdDev being biased and needing inflation for small series. This goes beyond annotations.

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

Conciseness4/5

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

Description is two sentences, front-loaded, and includes a key numerical caveat. Efficient despite including a formula.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given strong schema and annotations, the description adds crucial detail about output semantics and statistical interpretation. Reasonably complete for a fit tool with no output schema, though could note edge cases like insufficient data.

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?

Input schema covers all parameters with descriptive fields; description discusses the math but doesn't add new param details beyond schema. Baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

Clearly states it fits a log-linear trend to a historic series and names the series types. Though it could more explicitly distinguish from 'get_historic_series' (which retrieves data), it effectively communicates its function.

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

Usage Guidelines4/5

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

Explicitly describes composition with other tools to answer when hardware might reach a target, providing clear usage context. Does not list exclusions or when not to use, but the guidance is strong.

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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