Datris MCP server

Datris — The Data Control Plane for AI Agents

datris.ai · Documentation · MCP Registry · PyPI

Agents ask Datris for data. Datris finds it, acquires it, validates it, lands it in the stores you already run, and returns it with provenance — over MCP, without ever holding your keys. It sits beside your warehouse and lake; it doesn't replace them.

Why Datris?

Your agents already acquire, validate, and load data. Without a control plane, they do it badly. Datris puts that work behind one governed surface:

• One MCP door — 73 capabilities behind a single MCP server. Claude, Cursor, and any MCP-compatible agent learn one interface instead of 73 integrations
• Vault-brokered credentials — the agent references a secret by name and never holds a key; agent-written code runs in an isolated container with no keys inside
• Every run recorded — job state, row counts, and provenance for every run; every generated script versioned in git
• Durable state — pipelines and sync bookmarks live in the platform, not the chat, so regenerating a script never loses its place
• The operating loop — Acquire (AI-generated taps) → Validate (plain-English rules) → Land (multi-destination pipelines) → Observe (provenance and job state) → Explain & Repair (AI error explanation), with the same audit trail every time
• Self-host anywhere — on-prem, any cloud, or your laptop; 100% open-source infrastructure (MinIO, PostgreSQL, MongoDB, Kafka, Vault), AGPL-3.0, no managed service

Quick Start

You only need Docker. This pulls pre-built images and runtime files, seeds a .env, and starts the stack into ./datris — no git checkout required:

curl -fsSL https://get.datris.ai/install.sh | sh

Minimal install (laptop-friendly). The default stack runs about ten containers and the bundled embedding server downloads a 2.2 GB model on first boot. None of that is required. Three settings in .env cut it to eight small containers, no download, and roughly 3.5 GB of memory (4 GB of Docker memory is enough; the full stack wants 8 GB):

TEI_ENABLED=0               # skip the local embedding server and its 2.2 GB download
EMBEDDING_PROVIDER=openai   # semantic search via OpenAI (needs OPENAI_API_KEY); omit if you have no OpenAI key
POSTGRES_ENABLED=0          # optional: skip bundled Postgres; MongoDB stays as the destination

The installer sets the first two when you choose OpenAI embeddings. The datris server itself runs on a 2 GB heap by default. Details: Installation → Minimal install.

The install.sh installer is a POSIX shell script (macOS/Linux). On Windows, run it from WSL2 or Git Bash, or use the single-file Compose option below, which works natively in PowerShell.

Single file, no installer (works on Windows)

A fully self-contained Compose file — the init scripts and config are inlined, so nothing else is needed (requires Docker Compose ≥ 2.23):

# macOS / Linux
curl -O https://get.datris.ai/docker-compose.standalone.yml
ANTHROPIC_API_KEY=sk-ant-... docker compose -f docker-compose.standalone.yml up -d

Shortened. The full README is on GitHub.