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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false. The description goes beyond these by disclosing that the output is deterministic, returns a vector with labeled dimensions (chi statistics, energy distribution, gradient patterns), and that Level 1 adds phase coherence. This adds useful behavioral context without contradicting annotations.

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 sentences, each with a clear purpose: functionality (what it does), applicability (when to use), and output details (what you get). No redundancy, minimal fluff, and well-structured for quick comprehension.

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?

Given no output schema, the description adequately explains return format (deterministic vector, dimension counts, labeled components) and intended use cases. It does not include a concrete example or deeper interpretation of vector components, but the provided information is sufficient for an agent to understand scope and output. A 4 reflects the good coverage with minor 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?

Input schema covers 100% of parameters with descriptions (data, field_level, encoder_type). The description echoes the 52/62-dim distinction already in the schema and provides output-related labels, but no new parameter-specific semantics. Baseline of 3 is appropriate since the schema does the heavy lifting.

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 uses a specific verb ('Get') and resource ('physics embedding of any data item'), specifies dimension counts (52-dim at Level 0, 62-dim at Level 1), and lists concrete use cases (similarity search, clustering, drift detection). This clearly distinguishes it from sibling tools like waveguard_compare or waveguard_scan, which are specialized analysis 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 explicitly states when to use the tool (similarity search, clustering, baseline comparison, drift detection) and which data types are acceptable (JSON objects, token metrics, wallet activity, trading data). It lacks explicit 'when not to use' guidance or named alternatives, but the contextual guidance is clear enough for an agent to select it appropriately.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.