Convert text arrays into vector embeddings using GigaChat models. Input texts to get numerical representations for semantic search, clustering, or retrieval tasks.
Получение векторных представлений (эмбеддингов) текстов через GigaChat.
| Name | Required | Description | Default |
|---|---|---|---|
| input | Yes | Массив текстов для получения эмбеддингов | |
| model | No | Модель для эмбеддингов | Embeddings |
Changes observed during successful MCP inspections.
v3.0.1Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It only states what the tool does (get embeddings) without revealing return format, authentication requirements, rate limits, side effects, or any constraints. For a tool that likely returns substantial data (embedding vectors), this is a significant omission.
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, clear sentence that immediately states the purpose without any fluff. It is front-loaded with the action ('Получение векторных представлений') and efficiently conveys the essential information. No wasted words.
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?
The tool has no output schema and no annotations, so the description must explain the result and any usage constraints. It does not mention what the output looks like (e.g., array of vectors), any input limits (e.g., batch size), or error handling. For a tool that produces complex output, this is critically incomplete.
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 100% (both 'input' and 'model' have descriptions), so the schema already documents parameter meaning. The description adds no further semantic detail beyond what the schema provides, aligning with the baseline of 3 for full schema coverage.
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 explicitly states the tool returns vector representations (embeddings) for texts via GigaChat, using a specific verb ('Получение') and resource ('векторных представлений'). This clearly distinguishes it from siblings like chat, generate_image, or get_token_count. An agent can immediately understand the tool's unique role.
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 implies usage (use when you need embeddings) by stating the function, but it provides no explicit guidance on when to prefer this over alternatives, nor any exclusions or prerequisites. Usage is inferred from the purpose, qualifying as 'implied usage' rather than 'clear context' or explicit routing.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.