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

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

Output Schema

Name	Required	Description	Default
`items`	Yes	One series 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 series per hardware type.",
+      "items": {
+        "additionalProperties": {},
+        "properties": {
+          "data": {
+            "items": {
+              "additionalProperties": {},
+              "properties": {
+                "infidelity": {
+                  "description": "Fidelity series only: 2-qubit gate infidelity.",
+                  "type": "number"
+                },
+                "label": {
+                  "type": "string"
+                },
+                "qubitCount": {
+                  "description": "Qubit-count series only.",
+                  "type": "number"
+                },
+                "source": {
+                  "format": "uri",
+                  "type": "string"
+                },
+                "year": {
+                  "type": "number"
+                }
+              },
+              "required": [
+                "year",
+                "source"
+              ],
+              "type": "object"
+            },
+            "type": "array"
+          },
+          "name": {
+            "description": "Hardware type.",
+            "type": "string"
+          },
+          "note": {
+            "type": "string"
+          }
+        },
+        "required": [
+          "name",
+          "data"
+        ],
+        "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 that this is a safe, read-only, idempotent operation, so the bar is lower. The description adds useful behavioral context: the series is broken down by hardware type and each datapoint includes a source URL, which is not captured by annotations.

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?

Three concise sentences with the main purpose front-loaded. The examples and sibling pairing are compact and earn their place without repetition.

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?

The tool is simple, annotations cover its safety profile, the schema fully documents the only parameter, and an output schema exists. The description supplies enough context about data shape and provenance for an agent to call it correctly.

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?

Schema coverage is 100%, and the single parameter is an enum with its own description. The description restates the enum meanings but does not add syntax, format, or selection guidance beyond what the schema already provides, so the baseline of 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?

States a specific verb and resource: returns the full historic time series, with the two supported series types and hardware-type breakdown. It also distinguishes the tool from fit_historic_series by clarifying that this returns raw series data while fitting is handled by the sibling.

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?

Gives clear usage contexts — extrapolating trends and pairing with fit_historic_series for a log-linear fit. It does not explicitly state when not to use the tool, but the intended use cases are concrete enough for an agent to choose correctly.

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

All Quantum Expectations tools Inspect

Try in Browser

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

Resources

Need Help?