QMA N-representability on a 2×2 rational 2-RDM. rho2 and n are decimal strings. Float SDP refused. Same law as math_court domain qma_nrep.
| Name | Required | Description | Default |
|---|---|---|---|
| n | No | particle count as decimal string | |
| rho2 | No | a11,a12|a21,a22 as num/den rationals |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description bears the burden of behavioral disclosure. It adds useful behavior: refusing float SDP inputs and requiring decimal strings, and it ties the tool to a known law domain. However, it does not state the return format, possible errors, or side effects, leaving an agent partly to infer behavior from the tool name.
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?
The description is short and every sentence carries weight: subject, input representation, refusal behavior, and law identity. It is front-loaded and free of filler, though somewhat telegraphic and jargon-dense.
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?
There is no output schema and no annotations, so the description must carry more context. It provides enough to know the input encoding, but not what the tool returns, how failures surface, or how it relates to sibling verification tools. For a niche topic like QMA N-representability, this leaves significant gaps.
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?
Schema description coverage is 100%, so the baseline is 3. The description reinforces that rho2 and n are string-based and clarifies the 2×2 rational nature, but it does not add much beyond the schema. Its phrase 'decimal strings' is slightly ambiguous against the schema's 'num/den rationals', though not fundamentally misleading.
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 states a specific object and scope: QMA N-representability of a 2×2 rational 2-RDM, and anchors it with 'Same law as math_court domain qma_nrep', which helps distinguish it from the many verify_* siblings. It does not explicitly say 'verify' or 'check', but that verb is already in the tool name, so the intent is clear.
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?
There is no explicit guidance on when to choose this tool over alternatives or when not to use it. The note 'Float SDP refused' is an input constraint rather than a routing cue, and the math_court reference does not clarify whether math_court is an alternative or a dependency.
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.
Many verify_* tools are distinct, but there are overlapping clusters: math_court duplicates execute_2local_hamiltonian, route_spin_glass_manifold, and the quantum verifiers; project_affine_key, expose, verify_presented_pair, and the optional affine exposes in other tools blur together; weather and geometry tools also overlap. The detailed descriptions help a human, but an agent would likely struggle to choose between equivalent-seeming entry points.
Naming is mostly snake_case but otherwise inconsistent: some tools use dotted prefixes (atc.*, twin.robotics.*, weather.*), some use bare verbs (expose, lattice_op, math_court), some use noun phrases (corpus_bonds, feeds_catalog), and others mix prefixes with verbs (ide_rebuild_mesh, umc_resume). The verify_* family is consistent, but the overall set has no single predictable verb_noun pattern.
49 tools is far above the typical well-scoped server size and includes multiple near-duplicate paths to the same law (math_court, execute_*, route_*, verify_*). While not quite 50+, the count still feels like a sprawling kitchen-sink rather than a deliberate minimal surface.
The toolset covers a surprisingly wide range: QC verifiers, QMA laws, affine projections, corpus reads, weather, UMC state, and robotics IK. However, there are notable gaps for such a broad surface: no general court case lifecycle beyond expose/seal, no corpus content search, and no way to manage or update sealed artifacts; several areas have only entry-point coverage.