Infino AI MCP server

Infino MCP server

An MCP server for Infino — it lets an AI agent run keyword, semantic, hybrid, and SQL retrieval over your data on object storage — the retrieval layer for RAG, persistent agent memory, and search over your own files — from any MCP-compatible client (Claude Code, Claude Desktop, Cursor, VS Code, and others). Published on npm as @infino-ai/mcp-server and listed on the official MCP Registry as io.github.infino-ai/mcp-server (which propagates to catalogs like Smithery, Glama, and PulseMCP).

• Local embeddings, no key. Semantic search embeds queries with a local model — nothing leaves the machine for embedding.
• The agent owns the data. Every tool, writes included, is always available. On Infino Cloud the API key's capabilities decide what a connection may do; every tool carries MCP annotations so your client can ask before a destructive call.
• Local or hosted. Point it at a local path, your own bucket (S3, Azure, or any S3-compatible store), or a hosted Infino Cloud endpoint with an API key.
• The index is a valid Parquet file. A table stores the data and its search indexes in plain Parquet on the storage — open the same file with DuckDB or pyarrow. No export, no lock-in.

Contents

• Requirements
• Quick start
• Claude Code plugin (one-step install)
• Client setup
• Claude Code
• Claude Desktop
• Cursor
• VS Code
• Other MCP clients
• Configuration
• Environment variables
• Storage backends

Requirements

• Node.js ≥ 20 (the server runs as a Node process over stdio).
• An MCP-compatible client (Claude Code, Claude Desktop, Cursor, VS Code, …).
• Data reachable by Infino — a local directory, a bucket with credentials available in the environment, or a hosted Infino Cloud endpoint with an API key (see Storage backends).
• On first run the server downloads the local embedding model (~90 MB) once and caches it; subsequent runs are offline for embedding.

Quick start

The server is launched by your MCP client over stdio — you don't run it directly in normal use. Every client config follows the same shape: command npx -y @infino-ai/mcp-server, with configuration supplied via environment variables. Set INFINOMCPURI to the data you want to serve — a local path or a bucket URI. If it's omitted, the server uses a durable per-user directory (~/.infino/mcp) so data persists across restarts; point INFINOMCPURI at your own path or bucket to serve existing data.

{
  "command": "npx",
  "args": ["-y", "@infino-ai/mcp-server"],
  "env": {
    "INFINO_MCP_URI": "/Users/me/.infino/memory"
  }
}

To serve a hosted Infino Cloud database instead, point INFINOMCPURI at the https:/// endpoint and supply your API key. Everything else is identical:

Shortened. The full README is on GitHub.