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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
NameRequiredDescriptionDefault
seriesTypeYes"fidelity" → 2-qubit gate error rates; "qubit-count" → physical qubit counts.

Schema Changelog

Changes observed during successful MCP inspections.

  1. 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"
  2. First observed

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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