Compute text embeddings

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

Annotations indicate idempotency and non-destructiveness. The description adds return format details (structuredContent.output) and mentions model-specific shapes, providing behavioral context beyond 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?

The description is concise (about 120 words) and well-structured: purpose, use cases, args list, return value. No redundant sentences.

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 the tool has 5 parameters, no output schema, and nested objects, the description covers main parameters and return format adequately. It lacks explanation for download and timeout_ms, but schema descriptions compensate. An example would improve completeness.

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?

With 80% schema description coverage, the description adds value by explaining texts (independent embedding, max 256), model (curated list or custom), and extra_input with an example. Two parameters (download, timeout_ms) are not elaborated but have schema descriptions.

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 clearly states the tool converts text into numeric embedding vectors and lists specific use cases (RAG, semantic search, clustering, similarity scoring), distinguishing it from siblings that generate images, audio, etc.

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 use contexts but does not explicitly contrast with alternative tools or state when not to use it. However, the embeddings-focused purpose implicitly differentiates from siblings.

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