embed_text

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

With no annotations provided, the description carries full burden for behavioral disclosure. It reveals key traits: the tool is paid, produces 768-dim vectors, uses a specific model (nomic-embed-text), and has a limit of 64 texts. This is substantial transparency for a paid tool.

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 a single sentence containing all critical information: action, limit, model, output dimension, cost. No redundant words or filler. Front-loaded with the action and limit.

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 complexity (paid embedding, output vectors, cost), the description covers the main aspects: model, dimensionality, limit, cost. No output schema exists, but description mentions output dimension. However, it does not explain output format or when to prefer this tool over semantic siblings.

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 0%, so description must compensate. It adds meaning to the single parameter 'texts' by specifying the limit (up to 64 texts) and implying they are strings. However, it omits details like encoding or format requirements. The parameter name is clear, but description adds moderate value beyond the schema.

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 embeds texts, with specific verb 'Embed', resource 'texts', and quantitative details (up to 64 texts, 768-dim vectors, nomic-embed-text model). It distinguishes from siblings like classify, extract, etc., which perform different operations.

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 usage context (limit of 64 texts, paid costing ~$0.0003) but does not explicitly state when to use this tool versus alternatives such as classify or generate. It implies a dedicated embedding use case but lacks direct comparison or when-not-to-use guidance.

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