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).

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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

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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.

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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.

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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.)

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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.

OS	Path
macOS	~/Library/Application Support/Claude/claude_desktop_config.json
Windows	%APPDATA%\Claude\claude_desktop_config.json
Linux	~/.config/Claude/claude_desktop_config.json

{
  "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.

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Configuration

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

Environment variables

Variable	Required	Default	Description
INFINO_MCP_URI	No	~/.infino/mcp (persistent)	Data to serve: a local path (/Users/me/.infino/memory) or a bucket URI (s3://…, az://…). If unset, a durable per-user directory (~/.infino/mcp) is used so data persists across restarts; it falls back to an ephemeral in-process catalog (memory://) only if that directory can't be created.
INFINO_MCP_ENABLE_WRITES	No	off	When set (1/true/yes), exposes infino_add_documents and lets infino_sql run DDL/DML. Omit for a strictly read-only server.
INFINO_MCP_EMBED_PROVIDER	No	local	Embedding provider: local (Hugging Face transformers.js, no key, nothing leaves the machine) or openai (any OpenAI-compatible /embeddings endpoint — OpenAI, Azure OpenAI's /openai/v1 surface, or a compatible server). Inferred as openai when INFINO_MCP_EMBED_BASE_URL is set.
INFINO_MCP_EMBED_BASE_URL	With openai	—	Base URL of the OpenAI-compatible embeddings API, e.g. https://api.openai.com/v1 or https://<resource>.openai.azure.com/openai/v1. The server POSTs to <base>/embeddings.
INFINO_MCP_EMBED_API_KEY	No	—	API key for the openai provider. Sent as both Authorization: Bearer and api-key, so one value works for OpenAI and Azure OpenAI. Omit to call an unauthenticated or ambient-identity endpoint.
INFINO_MCP_EMBED_MODEL	No	Xenova/all-MiniLM-L6-v2 (local) · text-embedding-3-small (openai)	The embedding model. For local, a Hugging Face feature-extraction model; for openai, the model/deployment name. Must match the model that produced the table's stored vectors — and therefore its vector-index dimension (e.g. text-embedding-3-small is 1536-dim; the default local model is 384-dim).
INFINO_MCP_VALIDATE	No	off	When set (1/true/yes), probes the object store at startup so bad credentials or an unreachable bucket fail then instead of on the first search.

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.

Backend	Credentials
AWS S3	AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY (+ AWS_SESSION_TOKEN, AWS_REGION if used)
S3-compatible (R2/MinIO/B2)	AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY and AWS_ENDPOINT_URL
Azure Blob	AZURE_STORAGE_ACCOUNT, AZURE_STORAGE_KEY

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": "…"
}

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Tools

Tool	Arguments	What it does
infino_semantic_search	table, query, k, column?, vectorColumn?, columns?, filter?	Find passages by meaning — embeds the query with a local model (no key) and ranks by vector similarity. Handles paraphrase and synonyms. Optional filter ({column, query, mode?}) restricts the ranking to rows whose keyword column matches first (a pushdown pre-filter). Optional columns chooses which fields each hit returns (e.g. a path + line range to cite); defaults to the text column, with _id and score always included.
infino_keyword_search	table, query, k, column?, columns?	BM25 full-text search — for exact terms, identifiers, error codes, product names.
infino_hybrid_search	table, query, k, column?, vectorColumn?, columns?	Fused keyword + semantic search in one ranking pass — BM25 over the text column combined with vector similarity, so rows matching the literal terms and the meaning rank highest.
infino_token_match	table, query, column?, mode?, limit?	Unranked keyword filter — the set of rows whose text column contains the token(s). Use when you need the matches, not a relevance order.
infino_exact_match	table, value, column?, limit?	Unranked exact-equality filter over an indexed column (tag, status, id string).
infino_count	table, query, column?, mode?	Count how many rows match a keyword query, without fetching them — a fast tally over the text column. For the matching rows use infino_keyword_search or infino_token_match.
infino_sql	query	SQL for counts, filters, joins, aggregates. Read-only (single SELECT/WITH) by default; accepts any single statement when INFINO_MCP_ENABLE_WRITES is set.
infino_list_tables	—	List the tables in the connected catalog.
infino_describe_table	table	Column names and types for a table.
infino_add_documents	table, documents	Append rows (one call = one commit); embeds the text column for vector tables. Only when INFINO_MCP_ENABLE_WRITES is set.
infino_update_documents	table, predicate, documents	Replace the rows matching a SQL predicate with new documents, 1:1 (missing vectors are embedded). Durable storage only. Only when INFINO_MCP_ENABLE_WRITES is set.
infino_delete_documents	table, predicate	Delete the rows matching a SQL predicate. Durable storage only. Only when INFINO_MCP_ENABLE_WRITES is set.

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.

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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.

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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.

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Troubleshooting

Symptom	Likely cause / fix
Client shows no Infino tools	Server didn't start — check the client's MCP logs (stderr). Confirm npx is on PATH and INFINO_MCP_URI is set. Fully restart the client after editing config.
INFINO_MCP_URI is required	The env var isn't reaching the subprocess. In GUI clients, env must be inside the server's env block (the process won't inherit your shell).
add_documents not available	INFINO_MCP_ENABLE_WRITES isn't set, or the client wasn't restarted after setting it.
Slow first query	One-time embedding-model download (~90 MB). Subsequent runs use the cache.
Dimension / vector errors on semantic search	The table's vector index doesn't match the embedding model's dimension. Re-ingest, or set INFINO_MCP_EMBED_MODEL to the model the index was built with.
… in SQL isn't supported from the server yet	You called a search table function inside infino_sql. Use infino_semantic_search / infino_keyword_search instead.

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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

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License

Apache-2.0