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

With no annotations, the description carries the full behavioral burden. It mentions the 'signed-in user' context, which gestures at authentication, but does not disclose return format, embedding model/dimensions, batching semantics, rate limits, or how 'text' and 'texts' behave together.

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?

It is a single front-loaded sentence with no wasted words. The brevity is efficient, though it borders on under-specification rather than optimal conciseness for a tool with no annotations or output schema.

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?

The tool has no output schema and no annotations, so the description should explain the return value or behavior. Instead it only says 'Embed text,' leaving the agent without critical context about what embedding is returned or how the two parameters interact.

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 schema already documents 'text' as a natural-language query and 'texts' as an alternate list. The description adds no parameter semantics beyond that, which matches the baseline of 3 when 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 states a clear verb and resource: 'Embed text' for the signed-in user. It does not differentiate this tool from sibling search-pipeline tools such as search_smart, search_parse_intent, or search_rerank, but the core purpose 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?

There is no guidance on when to use search_embed versus other search-related siblings, nor any mention of prerequisites, exclusions, or whether this is an internal pipeline step. The agent receives only a bare statement of function.

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