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

With no annotations, the description is the primary behavioral disclosure. It specifies the output type (numeric embedding vector) and adds the multilingual capability, which are meaningful traits. It does not describe internal model details or edge-case behaviors, but the core transformation is 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 concise sentences, with the core action front-loaded. Every word contributes.

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 simple one-parameter embedding tool with no output schema, the description is complete: it states purpose, output type, and applicable use cases. The multilingual note adds useful context without bloat.

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?

The single parameter 'text' has full schema description coverage (100%), and the tool description largely mirrors the schema's explanation. The description adds no additional parameter semantics beyond what the schema provides.

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 ('Turn text into') and resource ('numeric embedding vector'), naming concrete use cases (semantic search, RAG, similarity). This clearly differentiates it from sibling text-processing tools like classify or summarize.

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 usage context by naming semantic search, RAG, and similarity as intended applications, but it stops short of explicitly contrasting with alternative tools or stating when not to use it.

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