Infino MCP server: retrieval for Claude, Cursor, and more

The @infino-ai/mcp-server package is a Model Context Protocol server for Infino. It gives an AI agent the same retrieval Infino exposes to the SDKs and the CLI — keyword (BM25), semantic (vector), hybrid, and SQL — as tools it can call in conversation, from any MCP client (Claude Code, Claude Desktop, Cursor, VS Code, and others). The tool descriptions are written so the model picks the right retrieval mode by question shape, so you rarely name a tool yourself. It connects to the same targets as the rest of Infino: a local path, your own object-storage bucket, or a hosted Infino Cloud database. Nothing extra runs — the server is the engine, in-process, behind the MCP transport.

Install

The server is launched by your MCP client over stdio; you don’t run it directly in normal use. Every client config is the same shape — command npx -y @infino-ai/mcp-server, with configuration supplied as environment variables.

npx -y @infino-ai/mcp-server

Connect

Point the server at your data with INFINO_MCP_URI. The scheme selects the backend:

`INFINO_MCP_URI`	Backend
`~/.infino/mcp` (default)	a durable per-user directory, so a fresh install persists with no config
`/path/to/data`	local disk
`s3://<bucket>/<prefix>`	Amazon S3 (or S3-compatible: R2, MinIO, Backblaze B2 — set `AWS_ENDPOINT_URL`)
`az://<container>/<prefix>`	Azure Blob
`https://<host>/<database>`	Infino Cloud, the hosted service

For a local path or bucket, storage credentials come from the standard AWS_* / AZURE_* environment variables (omit them to use ambient cloud identity). For a hosted https:// endpoint, authenticate with an API key in INFINO_API_KEY — no object-storage credentials are needed, since the platform owns the storage.

Environment variables

Variable	Required	Default	Description
`INFINO_MCP_URI`	No	`~/.infino/mcp`	Data to serve — see the table above.
`INFINO_API_KEY`	With a hosted URI	—	API key (`inf_…`) for a hosted `https://` endpoint. Ignored for local and bucket connections.
`INFINO_MCP_ENABLE_WRITES`	No	off	When set (`1`/`true`/`yes`), exposes the write tools and lets SQL run DDL/DML. Omit for a strictly read-only server.
`INFINO_MCP_EMBED_PROVIDER`	No	`local`	Query-embedding provider: `local` (Hugging Face, no key) or `openai` (any OpenAI-compatible `/embeddings` endpoint, including Azure OpenAI).
`INFINO_MCP_EMBED_MODEL`	No	`Xenova/all-MiniLM-L6-v2`	The embedding model — must match the model that produced the table’s stored vectors.

The full list of embedding and validation variables is in the infino-mcp README.

Client setup

Claude Code
Claude Desktop / Cursor
VS Code

Add the server with the CLI. Use --scope user to make it available in every project.

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

Or install the plugin, which wires the server plus a how-to-use skill and an /infino-search command in one step:

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

Add to the client’s MCP config (claude_desktop_config.json, or ~/.cursor/mcp.json), then restart:

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

VS Code (1.102+) reads servers from .vscode/mcp.json. 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"
      }
    }
  }
}

Any client that speaks MCP over stdio works — configure it to launch npx -y @infino-ai/mcp-server with the environment above.

Connect to Infino Cloud

To serve a hosted database, point INFINO_MCP_URI at the https://<host>/<database> endpoint and supply your API key. Everything else — the tools, the read-only default — is identical to a local connection:

{
  "command": "npx",
  "args": ["-y", "@infino-ai/mcp-server"],
  "env": {
    "INFINO_MCP_URI": "https://api.platform.infino.ws/my-database",
    "INFINO_API_KEY": "inf_…"
  }
}

Get the endpoint URL (the last path segment is your database) and an API key from the Infino Cloud console; see Authentication. Compaction and garbage collection are handled server-side on Infino Cloud, so they are not exposed as tools.

Tools

The server exposes discovery, search, and (optionally) write tools. Tool descriptions steer the model to the right one by question shape.

Tool	What it does
`infino_list_tables` / `infino_describe_table`	Discover the tables in the catalog and their columns.
`infino_keyword_search`	BM25 full-text search — literal terms, identifiers, error codes.
`infino_semantic_search`	Vector search by meaning; optional keyword pre-filter.
`infino_hybrid_search`	Fused keyword + vector ranking in one pass.
`infino_token_match` / `infino_exact_match`	Unranked keyword / exact-equality filters.
`infino_count`	Count rows matching a keyword query, without fetching them.
`infino_sql`	Counts, joins, aggregates, and filtering by column value.
`infino_add_documents` / `infino_update_documents` / `infino_delete_documents`	Writes — appended, replaced, or deleted rows. Only when `INFINO_MCP_ENABLE_WRITES` is set.

The server is read-only by default: the write tools aren’t advertised, and infino_sql accepts only a single SELECT/WITH. Set INFINO_MCP_ENABLE_WRITES to enable writes.

Semantic and hybrid search embed queries locally in the server (no embedding API key by default), so INFINO_MCP_EMBED_MODEL must match the model that produced the table’s stored vectors — this matters especially on a hosted or shared database that someone else ingested. See Embeddings.

​Install

​Connect

​Environment variables

​Client setup

​Connect to Infino Cloud

​Tools

​See also

Install

Connect

Environment variables

Client setup

Connect to Infino Cloud

Tools

See also