Quantum Expectations

Current Quantum Computers

list_current_quantum_computers

Read-onlyIdempotent

Return the representative-entry table of current SOTA quantum computers (id, hardware type, physical qubit count, 2-qubit error rate, connectivity class with a note on the coupling graph, source URL). Same data that powers the website's "Current Quantum Computers" table. For the model's capability prediction per entry in QUOPS units, read hardwareContext.currentQuantumComputers[].modelCapability from compute_expectation; for measured scores, list_quops_scores.

Input Schema

Name	Required	Description	Default
No arguments

Output Schema

Name	Required	Description	Default
`items`	Yes	One representative entry per hardware type.

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedOct 23, 2026

changedOutput schema / (root)

Previous value: -nullNew value: +{
+  "$schema": "https://json-schema.org/draft/2020-12/schema",
+  "additionalProperties": {},
+  "properties": {
+    "items": {
+      "description": "One representative entry per hardware type.",
+      "items": {
+        "additionalProperties": {},
+        "properties": {
+          "connectivity": {
+            "enum": [
+              "all-to-all",
+              "reconfigurable",
+              "2d-lattice"
+            ],
+            "type": "string"
+          },
+          "connectivityNote": {
+            "type": "string"
+          },
+          "errorRate": {
+            "description": "2-qubit gate error rate as a probability.",
+            "type": "number"
+          },
+          "errorRateDisplay": {
+            "type": "string"
+          },
+          "hardwareType": {
+            "type": "string"
+          },
+          "id": {
+            "description": "Stable alias accepted as hardwareId by the compute tools.",
+            "type": "string"
+          },
+          "physicalQubits": {
+            "maximum": 9007199254740991,
+            "minimum": -9007199254740991,
+            "type": "integer"
+          },
+          "source": {
+            "format": "uri",
+            "type": "string"
+          }
+        },
+        "required": [
+          "id",
+          "physicalQubits",
+          "errorRate",
+          "errorRateDisplay",
+          "hardwareType",
+          "connectivity",
+          "connectivityNote",
+          "source"
+        ],
+        "type": "object"
+      },
+      "type": "array"
+    }
+  },
+  "required": [
+    "items"
+  ],
+  "type": "object"
+}

Changed1 schema field changedAug 20, 2026

changedInput schema / $schema

Previous value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"

First observedJul 2, 2026

TDQS

A4.3/5.0

Behavior4/5

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

Annotations already declare readOnly, idempotent, non-destructive, closed-world, so the safety profile is covered. The description adds useful context beyond that: it is the same dataset behind the website table and it identifies the exact entry representation (representative entry per computer).

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?

Three sentences, front-loaded with the return payload, then provenance, then sibling routing. The first sentence is dense but every clause (columns, coupling-graph note, source URL) earns its place; no filler.

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?

An output schema exists, so the return-value enumeration in the description is somewhat redundant, but the sibling routing and the 'representative entry' semantics fill the gaps an agent actually needs. Nothing required to invoke it correctly is missing.

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?

Zero parameters, so the baseline is 4. The description introduces no parameter ambiguity; there is nothing to call incorrectly.

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?

States a specific verb (Return) plus a precisely scoped resource (representative-entry table of current SOTA quantum computers) and enumerates the columns returned. An agent can distinguish this from compare_hardware_scenarios or list_hardware_timings without opening the schema.

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 routes adjacent needs to siblings: modelCapability predictions come from compute_expectation, measured scores from list_quops_scores. It doesn't spell out when NOT to use this list (e.g. for historic data), but the alternative routing is clear enough to select correctly.

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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