Sealed bonds from the Affine.Earth corpus (GAIA_SPATIAL_BOND_WIRES). mined_from is LLM_WEIGHTS or LLVM_IR. Stateless: pin at_seq to make the read reproducible across cells, or omit it to read the moving head.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | decimal string, rows up to 8192; JSON numbers still admitted | |
| at_seq | No | decimal string, caller-pinned stream position; same value = same bytes | |
| sector | No | hex sector prefix |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden and explicitly discloses that the operation is stateless and that at_seq enables reproducible reads across cells. It does not describe the return shape or side effects beyond read intent, but for a read-only corpus accessor this is reasonably transparent.
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?
Two sentences with no filler: the resource, provenance values, statelessness, and the key invocation choice are all front-loaded and each clause earns its place.
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 tool with zero required parameters and full schema descriptions, the description covers the essential context: what is returned, provenance, and reproducibility semantics. It is slightly incomplete because it never clarifies what 'sealed bonds' are or how sector filtering relates to them, but it is adequate for correct invocation.
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 value on top by detailing what at_seq means behaviorally ('reproducible across cells' vs 'moving head'). It does not elaborate on limit or sector, but their schema descriptions already cover syntax and bounds.
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 starts with a concrete resource ('Sealed bonds from the Affine.Earth corpus (GAIA_SPATIAL_BOND_WIRES)') and names the data provenance values ('mined_from is LLM_WEIGHTS or LLVM_IR'), making the tool's function immediately identifiable. It is not a tautology and the specific corpus/bond resource separates it from the sibling corpus_* tools.
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 gives clear operational guidance by explaining how to get deterministic reads ('pin at_seq') versus reads of the moving head, which is a concrete context for invocation. It does not name alternative tools or state when not to use corpus_bonds, so it falls short of a 5.
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.