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

With no annotations provided, the description carries the full behavioral burden and succeeds admirably. It discloses the non-obvious behavior that the tool returns only a summary (item count, dimensions, input tokens), not the vectors themselves, and explains why (vectors too large for conversation). It also cautions the agent to use this for populating a store rather than reading values, which prevents a classic misuse.

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 dense sentences that each earn their place: the first establishes purpose and sibling relationship, the second explains the surprising return behavior and gives a usage directive. The key purpose is front-loaded, and there is no filler.

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 two-parameter tool with no output schema and no annotations, the description covers everything an agent needs to call it correctly: purpose, model, dimension, return shape (summary fields), batch guidance, and the follow-up sibling. The lack of an output schema is compensated by explicitly enumerating the returned summary fields.

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 schema documents both parameters. The description adds genuine value beyond the schema by explaining that batching strings in one call is cheaper, which informs how the 'input' parameter should be used. Encoding_format is already fully covered by the enum and default, and the description adds nothing needed there.

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 specific verb ('Turn text into...vectors'), names the exact model (qwen3-embedding), gives the output dimension (4096), and declares the use case (RAG or semantic search). It also differentiates itself from sibling rerank_documents by positioning it as the subsequent ordering step, so there is no ambiguity about what this tool does.

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?

The description provides clear context for when to use the tool: for RAG or semantic search, and to populate a vector store rather than read values. It also explains the relationship to the rerank_documents sibling ('then orders whatever a search over those vectors brings back'). It does not explicitly state when not to use it or name an alternative for the same job, so it stops 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.