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embed_text

Convert a text into a numerical vector embedding to enable similarity search, clustering, and classification. Supports doc and query embedding models.

Instructions

Получить эмбеддинг (векторное представление) одного текста.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesТекст для получения эмбеддинга
modelNoМодель эмбеддингов (text-search-doc, text-search-query)text-search-doc
Behavior2/5

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

Description is minimal and provides no behavioral details such as model options, output format, rate limits, or idempotency. Since no annotations are provided, the description carries full burden but fails to disclose these traits.

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

Conciseness4/5

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

Single sentence with clear action, no fluff. However, lacks structure or explicit breakdown of usage. Still efficient for its length.

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

Completeness2/5

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

With 2 parameters, no output schema, and siblings that overlap in capability, the description is insufficient. It does not explain the model parameter options or how output is used, leaving gaps for correct tool selection.

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

Parameters3/5

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

Schema description coverage is 100%, so the schema already documents both parameters. The description adds no additional meaning beyond what is in the schema, so baseline score of 3 is appropriate.

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?

Description clearly states it gets an embedding (vector representation) for a single text. The phrase 'одного текста' differentiates from sibling tool embed_documents which likely handles batches.

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

Usage Guidelines2/5

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

No guidance on when to use this tool versus alternatives. Given sibling tools like embed_documents, the description should explicitly mention that this is for single texts only, but it does not.

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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