Liquid MCP server

Liquid

Connect your AI agent to anything — with no connector to write or maintain.

Point Liquid at a URL or a database and it works out the interface for you: discovers its shape, maps it to the fields you asked for, and handles auth, pagination and normalization — typed records, no client code. When the upstream drifts, it re-maps and keeps going. The same small API — fetch · query · write · sense — reaches web APIs, databases, other agents (MCP/A2A), email, and even IoT and industrial systems (MQTT, Modbus, OPC UA, BACnet). An LLM does the learning at setup (and on drift); the data path itself makes no model call.

What an agent can reach through Liquid

One agent-facing API (fetch · query · write · sense) over everything an agent might need to touch — Liquid figures out how to talk to it so the agent doesn't have to. It's the agent's senses and hands: fetch/query probe, sense perceives a live event stream, write acts on the world.

SSE/NDJSON streams, MQTT (IoT pub/sub — subscribe to sense, publish to act)

• Web APIs & messaging — REST/JSON, GraphQL, SOAP/WSDL, gRPC, WebSocket,

Gmail API (OAuth2): read a mailbox, sense new mail as it arrives, and send

• Email — IMAP/SMTP (any provider, app-password or OAuth2 XOAUTH2) and the

native subscriptions) for the factory floor; BACnet for buildings (HVAC/BMS) — read, write, and sense

• Industrial / OT — Modbus (PLCs, sensors) and OPC UA (Industry-4.0 nodes,

shell, act with input/am

• Android devices — phones, TV boxes, kiosks via ADB: sense logcat, read

Neo4j (graph), MongoDB (documents), Redis (key-value)

• Other agents & tools — any MCP server, A2A agents, ChatGPT-plugin manifests
• Databases — Postgres (+ pgvector), MySQL/MariaDB, SQLite, DuckDB, SQL Server,

connectors: Telegram (perceive messages, send replies), Home Assistant (perceive a whole smart home's state changes, act via call_service — lights, locks, media), and Smartcar (perceive a connected vehicle across ~30 brands — location/battery/fuel — and act: lock/unlock, charge)

• People, places & things — a human, a home, or a car as a node via

Point it at a https://… endpoint, a postgres://… / mongodb://… / redis://… DSN, a grpc://… target, or another MCP server — discovery identifies the interface, learns its shape, and hands your agent typed records. The same fetch/query/write works regardless of what's underneath. No per-service connector to hand-write; the integration maintains itself when the upstream changes.

# A web API it has never seen — no spec, no connector, no auth
adapter = await liquid.get_or_create(
    "https://api.openbrewerydb.org/v1/breweries",
    target_model={"name": "str", "city": "str", "country": "str"},
    auto_approve=True,
)
breweries = await liquid.fetch(adapter)            # typed records

# A database is just another interface — same API, and it writes too
db = await liquid.get_or_create("postgresql://reader@host/shop",
                                target_model={"id": "int", "email": "str"},
                                auto_approve=True)
orders = await liquid.fetch(db, "/public/orders")
await liquid.write(db, "/public/orders", op="insert",
                   values={"email": "a@b.com", "total_cents": 9900},
                   allow_write=True)               # opt-in; mutates the store

Shortened. The full README is on GitHub.