embed_text
Generate text embeddings using Gemini models to convert text into numerical vectors for AI applications like semantic search and similarity analysis.
Instructions
Generate embeddings for text using Gemini embedding models
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | Text to generate embeddings for | |
| model | No | Embedding model to use | text-embedding-004 |
Implementation Reference
- src/enhanced-stdio-server.ts:780-816 (handler)The core handler function that executes the embed_text tool by calling Google Gemini's embedContent API to generate text embeddings.
private async embedText(id: any, args: any): Promise<MCPResponse> { try { const model = args.model || 'text-embedding-004'; const result = await this.genAI.models.embedContent({ model, contents: args.text }); return { jsonrpc: '2.0', id, result: { content: [{ type: 'text', text: JSON.stringify({ embedding: result.embeddings?.[0]?.values || [], model }) }], metadata: { model, dimensions: result.embeddings?.[0]?.values?.length || 0 } } }; } catch (error) { return { jsonrpc: '2.0', id, error: { code: -32603, message: error instanceof Error ? error.message : 'Internal error' } }; } } - src/enhanced-stdio-server.ts:339-358 (registration)Tool registration in the getAvailableTools() method, defining the tool name, description, and input schema.
{ name: 'embed_text', description: 'Generate embeddings for text using Gemini embedding models', inputSchema: { type: 'object', properties: { text: { type: 'string', description: 'Text to generate embeddings for' }, model: { type: 'string', description: 'Embedding model to use', enum: ['text-embedding-004', 'text-multilingual-embedding-002'], default: 'text-embedding-004' } }, required: ['text'] } }, - src/enhanced-stdio-server.ts:342-358 (schema)Input schema definition for the embed_text tool, specifying required 'text' parameter and optional embedding model.
inputSchema: { type: 'object', properties: { text: { type: 'string', description: 'Text to generate embeddings for' }, model: { type: 'string', description: 'Embedding model to use', enum: ['text-embedding-004', 'text-multilingual-embedding-002'], default: 'text-embedding-004' } }, required: ['text'] } }, - src/enhanced-stdio-server.ts:486-487 (registration)Dispatch logic in handleToolCall() switch statement that routes embed_text calls to the handler.
case 'embed_text': return await this.embedText(request.id, args);