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Voyageai Cli

@mrlynn

Repository

About Voyageai Cli

MCP server for Voyage AI embeddings, reranking, and MongoDB Atlas Vector Search. Provides 11 tools for semantic search, document ingestion, cost estimation, and model exploration. Full RAG pipeline: chunk, embed, vector search, and rerank from any MCP client.

#voyage-ai#embeddings#vector-search#mongodb#rag#reranking#semantic-search#retrieval

Basic information

Category

Memory & Knowledge

Transports

stdio

Publisher

mrlynn

Submitted by

Michael Lynn

Config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "vai": {
      "command": "npx",
      "args": [
        "voyageai-cli",
        "mcp-server"
      ],
      "env": {
        "VOYAGE_API_KEY": "<YOUR_VOYAGE_API_KEY>"
      }
    }
  }
}

Tools

No tools detected

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

What is Voyageai Cli?

An MCP server that gives AI agents access to Voyage AI's embedding and reranking models plus MongoDB Atlas Vector Search. It is designed for building RAG pipelines directly from an AI agent without leaving the agent interface.

How to use Voyageai Cli?

Install globally with npm install -g voyageai-cli, then run vai mcp install claude-code or vai mcp install all to configure the server for your agent. Alternatively, use npx without installation. Requirements: Node.js 20+, a Voyage AI API key (free at dash.voyageai.com), and a MongoDB Atlas cluster for retrieval/ingestion tools (utility tools work without it).

Key features of Voyageai Cli

  • 11 tools covering retrieval, embedding, management, utility, and ingestion
  • Full RAG pipeline: vai_query, vai_search, vai_rerank
  • Embedding generation with vai_embed and cosine similarity with vai_similarity
  • List indexes (vai_collections) and browse models (vai_models)
  • Learning tools: vai_topics, vai_explain, and cost calculator vai_estimate
  • Ingestion pipeline: chunk, embed, and store documents via vai_ingest

Use cases of Voyageai Cli

  • Build end-to-end RAG applications from within an AI agent
  • Perform vector search on MongoDB Atlas collections
  • Rerank search results for improved relevance
  • Generate embeddings for custom data pipelines
  • Estimate costs before large-scale ingestion

FAQ from Voyageai Cli

What dependencies and runtime does Voyageai Cli require?

Node.js version 20 or later, a Voyage AI API key (free at dash.voyageai.com), and optionally a MongoDB Atlas cluster for retrieval and ingestion tools.

How do I install Voyageai Cli?

Run npm install -g voyageai-cli, then use vai mcp install claude-code or vai mcp install all to connect it to your agent. You can also use npx without installing.

What tools are available in Voyageai Cli?

Eleven tools: vai_query, vai_search, vai_rerank (retrieval); vai_embed, vai_similarity (embedding); vai_collections, vai_models (management); vai_topics, vai_explain, vai_estimate (utility); and vai_ingest (ingestion).

Where can I find documentation and support?

Documentation is at https://github.com/mrlynn/voyageai-cli/blob/main/docs/mcp-server.md. General information is at https://vai.mlynn.org. The npm package is at https://www.npmjs.com/package/voyageai-cli.

What transport does Voyageai Cli use?

The README does not specify transport details. It indicates integration via MCP (Model Context Protocol) commands for standard agent communication.

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