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embed

Convert text into embedding vectors for semantic analysis, supporting single strings or batch lists with optional normalization.

Instructions

Generate an embedding vector for the given text (OpenAI-compatible /v1/embeddings).

input can be a single string or a list of strings for batch embedding.

Examples: embed(model="text-embedding-nomic-embed-text-v1.5", input="hello world") embed(model="text-embedding-nomic-embed-text-v1.5", input=["hello", "world"], normalize=True)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
inputYes
modelYes
normalizeNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior3/5

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

No annotations provided, so description carries full burden. It discloses input types and batch behavior but lacks details on idempotency, error handling, or side effects.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Extremely concise: one sentence stating purpose, one phrase on input types, and two illustrative examples. No wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Covers key functionality with examples. Could mention model naming conventions or where to find available models, but given output schema exists, return values are likely documented.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, but description clarifies the 'input' parameter accepts string or list of strings, and demonstrates 'normalize' usage. Adds significant meaning beyond schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Explicitly states it generates an embedding vector, mentions OpenAI compatibility, and provides examples. Distinct from sibling tools like chat and complete.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No explicit when-to-use or alternative guidance. The description implies text embedding tasks but doesn't contrast with other tools like chat or complete.

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

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