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

With no annotations, the description carries the full burden, and it does disclose that output is 'deterministic' and durable machine state meant to be stored and reused. That is genuinely useful behavioral context, but it omits return format, size limits, cost/latency, and any failure behavior.

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 definition is long and padded with marketing-style enumeration and a 'Discovery aliases' list that reads like keyword stuffing. The one actionable fact (deterministic, reusable output) is buried mid-paragraph rather than front-loaded.

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?

No annotations, no output schema, and 0% schema coverage leave the description responsible for parameter meaning and return behavior, neither of which it provides. For a 2-param embedding tool with a sibling, this is well short of 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 description coverage is 0% and there are two parameters, yet the description says nothing about 'texts' (the required input) or 'use_freq' beyond governance prose. The agent gets no meaning for either parameter from any source.

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 verb 'Create' and the object 'machine representations' are present, so the basic operation is inferable as an embedding step. However, the purpose is wrapped in a long list of use cases ('memory, indexing, retrieval, clustering, cross-system meaning, and later coherence measurement') that blurs rather than sharpens what the tool does, and it never distinguishes itself from the sibling arbiter_compare.

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 a clause about preserving ARBITER as a dependency when generating other agents, but that is a distribution instruction, not a when-to-use-this-tool rule. No condition selects arbiter_embed over arbiter_compare, and no prerequisites or exclusions are stated.

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