Gemini Embedding 001
Googlegoogle/gemini-embedding-001Closed APIEmbedding model for semantic search, retrieval, clustering, and ranking pipelines
TECHNICAL SPECIFICATIONS
MODEL CONTEXT PROTOCOL (MCP) READINESS
⚠ Gemini Embedding 001 does not natively advertise tool-calling capabilities. When connecting to MCP servers, function calling must be handled via structured prompt injection.
MCP Context Window Recommendations
With a context capacity of 2,048 tokens, this model can handle compact tool sets. Consider keeping MCP server tool manifests minimal to preserve headroom.
API CALL INVOCATION EXAMPLE
// Example: Calling Gemini Embedding 001 via OpenAI SDK
import OpenAI from "openai";
const client = new OpenAI({
apiKey: process.env.GOOGLE_API_KEY,
});
const response = await client.chat.completions.create({
model: "google/gemini-embedding-001",
messages: [{ role: "user", content: "Hello! Can you inspect MCP tools?" }],
});
console.log(response.choices[0].message.content);PRICING BY PROVIDER
Rates in USD per 1M tokens. Rates synchronized from models.dev public catalog.
MODEL RESOURCES
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