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Quantum Expectations

qLDPC Code Catalog

list_qldpc_codes
Read-onlyIdempotent

Return the catalog of supported qLDPC 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

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

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

A4.4/5.0
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 nuance beyond these: it explains the fitting form (p^alpha * exp(c0 + c1*p + c2*p^2) for p <= threshold), notes that c1=c2=0 means a plain power law, and emphasizes that `provenance` and `caveats` carry source-level qualifications and that some values may be assumed or differ between sources. This warns the agent not to treat the numbers as universally firm, which is valuable. No contradiction 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.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, dense paragraph that front-loads the primary action and then logically flows from field enumeration to the fitting formula, provenance, caveats, and usage integration. Every sentence adds information — there is no filler or repetition. It is longer than the typical tool description, but given the richness of the catalog contents, it earns its length. A slightly more structured layout (e.g., bullet points) could improve scannability, but it remains efficient.

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 zero parameters and no output schema, the description carries the full burden of telling the agent what to expect. It exhaustively lists all returned fields, explains the mathematical fitting form and its special cases, clarifies the meaning of `provenance` and `caveats`, and instructs on how to use the returned `id` with `compute_expectation`. Nothing an agent needs to correctly invoke the tool or interpret its result 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?

The input schema has zero parameters, so there is nothing for the description to clarify about parameters. Per the rubric, 0 params yields a baseline of 4. The description does not attempt to document parameters but instead explains the output structure and the meaning of each field, which is beyond the schema's scope and thus not penalized here.

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 opens with 'Return the catalog of supported qLDPC codes' — a specific verb and resource. It enumerates the exact fields returned (id, label, family, n, k, d, circuitLevelDistance, ancilla counts, roundsPerLogicalOp, threshold, fitCoefficients, logicalErrorExponent, source URLs, provenance, caveats), leaving no ambiguity about what the tool does. It also implicitly differentiates from sibling list tools (e.g., list_current_quantum_computers) by focusing exclusively on qLDPC codes.

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?

The description provides concrete usage guidance: 'Read it before quoting a code's logical error rate as firm' and 'Use a code's `id` as the `errorCorrectionCode` input to `compute_expectation`.' This tells the agent when to consult the catalog and how to feed its output into a sibling tool. It does not explicitly state when *not* to use this tool or name alternative catalog tools, but the context is sufficient for an agent to understand its role.

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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