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, 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).

• No API key. Semantic search embeds queries with a local model — nothing leaves the machine for embedding.

• Read-only by default. Writes and full SQL are opt-in behind a single environment flag.

• Bring your own storage. Point it at a local path or your own bucket (S3, Azure, or any S3-compatible store).

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

• Tools

• Security & data handling

• How retrieval works

• Troubleshooting

• Local development

• License

Related MCP server: local-docs-mcp

Requirements

• Node.js ≥ 18 (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, or a bucket with credentials available in the environment (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 INFINO_MCP_URI 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 INFINO_MCP_URI 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"
  }
}

The sections below show the exact place each client expects this block.

Claude Code plugin (one-step install)

For Claude Code, this repo is also a plugin marketplace. Installing the plugin wires up the MCP server plus a how-to-use skill and an /infino-search command in one step — no JSON to edit. Inside Claude Code:

/plugin marketplace add infino-ai/infino-mcp
/plugin install infino@infino-ai

On enable you'll be prompted for your Infino data URI (INFINO_MCP_URI) and whether to enable writes. That's it — the infino_* tools, the using-infino skill, and /infino-search <query> are then available. (Other clients: use the Client setup configs below.)

Client setup

Claude Code

Add the server with the CLI. Use --scope user to make it available in every project, or --scope project to commit it to the repo (writes a shared .mcp.json); the default scope is local (this project only).

claude mcp add infino \
  --scope user \
  -e INFINO_MCP_URI=/Users/me/.infino/memory \
  -- npx -y @infino-ai/mcp-server

Add more knobs with repeated -e flags, e.g. -e INFINO_MCP_ENABLE_WRITES=true. Verify with:

claude mcp list
claude mcp get infino

Claude Desktop

Edit the configuration file (create it if it doesn't exist), then fully restart Claude Desktop.

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

Cursor

Add the server to ~/.cursor/mcp.json (available in all projects) or <project>/.cursor/mcp.json (this project only), then reload. The format matches Claude Desktop:

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

VS Code

VS Code (1.102+) reads MCP servers from .vscode/mcp.json in the workspace (or your user mcp.json via the command palette → MCP: Open User Configuration). Note the top-level key is servers and each entry declares "type": "stdio":

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

Other MCP clients

Any client that speaks MCP over stdio works. Configure it to launch:

command: npx
args:    -y @infino-ai/mcp-server
env:     INFINO_MCP_URI=<path-or-bucket-uri>   (plus any options below)

Logs are written to stderr so they never corrupt the JSON-RPC stream on stdout — point your client's log capture there when debugging.

Configuration

All configuration is via environment variables — there are no config files and no command-line flags to manage.

Environment variables

Cloud credentials are read from the standard provider environment variables — the server maps them to the store's config and introduces no credential vars of its own. Omit them entirely to use ambient cloud identity (an IAM instance role or Azure managed identity).

Serving a catalog embedded with OpenAI / Azure OpenAI. If your tables were vectorized with a hosted embedding model rather than the local default, point the server at that same model so query and document vectors align:

"env": {
  "INFINO_MCP_URI": "s3://my-bucket/infino",
  "INFINO_MCP_EMBED_PROVIDER": "openai",
  "INFINO_MCP_EMBED_BASE_URL": "https://my-resource.openai.azure.com/openai/v1",
  "INFINO_MCP_EMBED_API_KEY": "…",
  "INFINO_MCP_EMBED_MODEL": "text-embedding-3-small"
}

The model must match what produced the stored vectors — a mismatch yields meaningless similarity or a dimension error. Keyword and SQL search are unaffected by the embedder.

Storage backends

// Local directory
"env": { "INFINO_MCP_URI": "/Users/me/.infino/memory" }

// AWS S3 — ambient AWS_* credentials, default endpoint
"env": {
  "INFINO_MCP_URI": "s3://my-bucket/infino",
  "AWS_ACCESS_KEY_ID": "…",
  "AWS_SECRET_ACCESS_KEY": "…"
}

// S3-compatible (Cloudflare R2 / MinIO / Backblaze B2) — custom endpoint
"env": {
  "INFINO_MCP_URI": "s3://my-bucket/infino",
  "AWS_ENDPOINT_URL": "https://<account>.r2.cloudflarestorage.com",
  "AWS_ACCESS_KEY_ID": "…",
  "AWS_SECRET_ACCESS_KEY": "…"
}

// Azure Blob
"env": {
  "INFINO_MCP_URI": "az://my-container/infino",
  "AZURE_STORAGE_ACCOUNT": "…",
  "AZURE_STORAGE_KEY": "…"
}

Tools

The engine's search table functions (bm25_search, vector_search, hybrid_search, …) are not callable from infino_sql — retrieval goes through the dedicated search tools above, which embed and project for you. infino_sql is for filters, joins, and aggregates.

Security & data handling

This server is designed to run locally, beside the client, and to keep data and credentials on the user's machine.

• Local execution. It runs as a subprocess of your MCP client over stdio. There is no network listener and no remote service.

• No data sent for embedding. Query and document embedding uses a local model — text is never sent to a third-party embedding API. There is no API key to provision or leak.

• Credentials stay in the environment. Storage credentials are read from standard provider environment variables and used only to reach the bucket you configured. They are never logged or returned in tool output.

• Read-only by default. Without INFINO_MCP_ENABLE_WRITES, the write tool is not even advertised to the agent, and infino_sql rejects anything but a single SELECT/WITH. Enable writes deliberately, and prefer scoping the server to data the agent is allowed to modify.

• Least privilege. Point INFINO_MCP_URI at the narrowest dataset the task needs, and supply storage credentials scoped to that bucket/prefix.

How retrieval works

Semantic search embeds locally with Hugging Face transformers.js (all-MiniLM-L6-v2, 384-dim by default; override with INFINO_MCP_EMBED_MODEL). The server embeds both the documents it ingests (via infino_add_documents) and your queries with the same model, so they align in the same vector space.

If you change INFINO_MCP_EMBED_MODEL, the table's vector index must match the new model's dimension — embeddings produced by different models are not comparable, and a dimension mismatch will fail at search time.

Troubleshooting

Local development

The server depends on the published @infino-ai/infino Node binding, which resolves from public npm like any other dependency.

npm install
npm run build
INFINO_MCP_URI=/path/to/data node dist/index.js   # runs on stdio

Point a client at node /absolute/path/dist/index.js over stdio to dogfood a local build, or use the MCP Inspector:

npx @modelcontextprotocol/inspector node dist/index.js

License

Apache-2.0