Quantum Expectations

qLDPC Code Catalog (Quantum Low-Density Parity-Check)

list_qldpc_codes

Read-onlyIdempotent

Return the catalog of supported qLDPC (quantum low-density parity-check) error-correction codes (id, label, family, n, k, d, circuitLevelDistance, ancilla counts, roundsPerLogicalOp, threshold, fitCoefficients {c0, c1, c2}, logicalErrorExponent [alpha = d_circ/2], source URLs, provenance, caveats). The per-block, per-syndrome-cycle logical error rate is p^alpha * exp(c0 + c1p + c2p^2) for p <= threshold (the source paper's own fitting form; c1 = c2 = 0 means a plain power law). provenance names the table or section each constant was read from. caveats is an array of source-level qualifications on the entry's constants (loose distance bounds, values a source marks as assumed, numbers that differ between sources); empty when the sources carry none. Read it before quoting a code's logical error rate as firm. Use a code's id as the errorCorrectionCode input to compute_expectation.

Input Schema

Name	Required	Description	Default
No arguments

Output Schema

Name	Required	Description	Default
`items`	Yes	Supported qLDPC codes.

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": "Supported qLDPC codes.",
+      "items": {
+        "additionalProperties": {},
+        "properties": {
+          "caveats": {
+            "items": {
+              "type": "string"
+            },
+            "type": "array"
+          },
+          "checkAncillasPerBlock": {
+            "type": "number"
+          },
+          "circuitLevelDistance": {
+            "type": "number"
+          },
+          "d": {
+            "maximum": 9007199254740991,
+            "minimum": -9007199254740991,
+            "type": "integer"
+          },
+          "family": {
+            "type": "string"
+          },
+          "fitCoefficients": {
+            "additionalProperties": {},
+            "properties": {
+              "c0": {
+                "type": "number"
+              },
+              "c1": {
+                "type": "number"
+              },
+              "c2": {
+                "type": "number"
+              }
+            },
+            "required": [
+              "c0",
+              "c1",
+              "c2"
+            ],
+            "type": "object"
+          },
+          "id": {
+            "description": "Accepted as errorCorrectionCode by compute_expectation.",
+            "type": "string"
+          },
+          "k": {
+            "maximum": 9007199254740991,
+            "minimum": -9007199254740991,
+            "type": "integer"
+          },
+          "label": {
+            "type": "string"
+          },
+          "logicAncillasPerBlock": {
+            "type": "number"
+          },
+          "logicalErrorExponent": {
+            "type": "number"
+          },
+          "n": {
+            "maximum": 9007199254740991,
+            "minimum": -9007199254740991,
+            "type": "integer"
+          },
+          "provenance": {
+            "additionalProperties": {
+              "type": "string"
+            },
+            "propertyNames": {
+              "type": "string"
+            },
+            "type": "object"
+          },
+          "roundsPerLogicalOp": {
+            "type": "number"
+          },
+          "source": {
+            "format": "uri",
+            "type": "string"
+          },
+          "threshold": {
+            "type": "number"
+          }
+        },
+        "required": [
+          "id",
+          "label",
+          "family",
+          "n",
+          "k",
+          "d",
+          "circuitLevelDistance",
+          "checkAncillasPerBlock",
+          "logicAncillasPerBlock",
+          "roundsPerLogicalOp",
+          "threshold",
+          "fitCoefficients",
+          "logicalErrorExponent",
+          "provenance",
+          "source",
+          "caveats"
+        ],
+        "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

Behavior5/5

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

Annotations already declare a safe, read-only, idempotent, closed-world operation. The description adds substantial behavioral context beyond annotations: the logical error-rate formula, the meaning of `provenance`, and the `caveats` array semantics, including that it is empty when sources carry no qualifications.

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?

The description is front-loaded with the tool's purpose and then supplies dense but useful detail about fields, the formula, and caveats. It is somewhat long and lists output fields despite an output schema, but the information is relevant and structured.

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?

Despite having no parameters, the tool has complex semantics that the description handles well, including the formula, caveat/provenance interpretation, and downstream use of `id`. With an output schema present, the description need not explain raw return values, and its added semantic coverage is sufficient.

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?

The tool has zero parameters, which is the baseline case for a score of 4. Parameter semantics are therefore not a concern, and the description appropriately focuses on the catalog contents and downstream `id` usage.

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?

The description gives a specific verb and resource: returning the catalog of supported qLDPC codes, and it names the key fields returned. It is clearly distinct in subject matter from sibling list_* tools, though it does not explicitly contrast itself with them.

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?

It gives clear usage guidance: read the catalog before quoting a code's logical error rate as firm, and use a code's `id` as the `errorCorrectionCode` input to `compute_expectation`. It does not state when not to use it, but the context is otherwise clear.

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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