list_current_quantum_computersReturn the representative-entry table of current SOTA quantum computers (id, hardware type, physical qubit count, 2-qubit error rate). Same data that powers the website's "Current Quantum Computers" table.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations (readOnlyHint=true, idempotentHint=true, destructiveHint=false) already provide the safety profile. The description adds context by specifying the exact fields returned (id, hardware type, physical qubit count, 2-qubit error rate) and that it mirrors the website table, which gives the agent a clear expectation of the data source and content. No contradictions; behavior is consistent 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, directly informative, no filler. Front-loaded with the main purpose and then adds provenance. Perfectly concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a zero-parameter read-only tool, this description is complete: it specifies what is returned (fields), the source (website table), and implicitly the scope (current SOTA). The annotations cover safety properties, so no further behavioral detail is needed. The description is sufficient for an agent to select and use it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
There are zero parameters, so the baseline is 4. The description goes slightly beyond the schema by specifying the returned fieldsced it adds no parameter-specific guidance, but none is needed. The mention of 'representative-entry table' clarifies that it's a summary, not exhaustive, which is useful.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool returns a representative-entry table of current SOTA quantum computers with specific fields (id, hardware type, qubit count, error rate). It distinguishes from siblings by focusing on 'current SOTA' and referencing the website's table, making its purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides clear context by indicating it returns the same data as the website's table, implying it is the canonical source for current quantum computer specs. However, it doesn't explicitly mention when not to use it or name alternative tools, so it lacks explicit exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.
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.
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.
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.