#P exact integer permanent, n≤3. matrix is a decimal-string grid. Float permanent refused. Same law as math_court domain qma_permanent.
| Name | Required | Description | Default |
|---|---|---|---|
| matrix | No | rows | cells , integer entries |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full behavioral burden. It discloses exact integer semantics, the n≤3 limit, and float refusal, which are valuable. But it does not state the return format, failure behavior, or how an invalid matrix string is handled, and the 'Same law as...' clause is opaque without domain context.
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?
Three terse sentences front-load the operation and constraints with no redundant restatement of the schema. The 'Same law...' clause is jargon, but it is compact and does not bloat the description.
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?
For a one-parameter compute tool with no output schema, the core constraints are present. However, an agent still lacks a concrete return-type statement, an example, and failure behavior, and the domain-law reference assumes external knowledge. Adequate but not fully complete.
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 coverage is 100%, so the baseline is 3. The description adds useful constraints by calling the matrix a decimal-string grid, forbidding floats, and capping n≤3, but it does not clarify required status (schema says required=[]) or provide a concrete delimiter example.
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 phrase '#P exact integer permanent' specifies a precise computational task, and 'n≤3' sets the scope, distinguishing it from the other execute_* siblings. No explicit verb like 'computes' appears, but the meaning is unambiguous.
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?
It conveys when-not conditions: 'Float permanent refused' and 'n≤3'. However, it neither names an alternative tool nor explains when to prefer this over execute_transition or execute_2local_hamiltonian, so routing guidance is only implicit.
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.