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Datris

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MCP server for AI-driven data pipeline operations — ingest, validate, transform, analyze, and query data. Tools covering ETL, AI data quality, vector search, Po

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MCP server for AI-driven data pipeline operations — ingest, validate, transform, analyze, and query data. Tools covering ETL, AI data quality, vector search, PostgreSQL, MongoDB, Snowflake, Databricks, Kafka, S3/MinIO, HashiCorp Vault, Qdrant, Weaviate, Milvus, Chroma, pgvector.

README

PyPI MCP Registry Docker Hub License

datris.ai · Documentation · MCP Registry · PyPI

Ingest, validate, transform, store, and retrieve your data — whether you're an AI agent talking through MCP or a developer writing config. One platform for both.

Why Datris?

  • Agent-native — Built-in MCP server with 47 tools. Claude, Cursor, and any MCP-compatible agent can operate pipelines through natural conversation
  • Taps — AI-generated Python scripts that fetch data from external sources (APIs, web scraping, databases) and push it into pipelines. Describe what you want, Datris generates the script. Includes AI diagnosis, CRON scheduling, and credentials via Vault
  • AI at every stage — AI data quality, AI transformations, AI schema generation, AI profiling, AI error explanation, natural language queries, RAG
  • No vendor lock-in — 100% open-source infrastructure (MinIO, PostgreSQL, MongoDB, Kafka, Vault). Runs anywhere Docker does
  • Configuration-driven — Define pipelines through JSON. No code required

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

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
# Windows (PowerShell) — use curl.exe, and set the key with $env:
curl.exe -O https://get.datris.ai/docker-compose.standalone.yml
$env:ANTHROPIC_API_KEY="sk-ant-..."
docker compose -f docker-compose.standalone.yml up -d
From a git clone
git clone https://github.com/datris/datris-platform-oss.git
cd datris-platform-oss
cp .env.example .env       # Add your ANTHROPIC_API_KEY and/or OPENAI_API_KEY
docker compose up -d

UI: http://localhost:4200 · API: http://localhost:8080

Connect an AI Agent

Add to your MCP client config (Claude Desktop, Claude Code, Cursor, etc.). With the Docker stack running, the npx mcp-remote stdio bridge connects to the bundled MCP server on port 3000 — your client appears in the Datris UI Agent Monitor tab with live tool-call streaming:

{
  "mcpServers": {
    "datris": {
      "command": "npx",
      "args": ["-y", "mcp-remote", "http://localhost:3000/sse", "--transport", "sse-only"]
    }
  }
}

Paste-and-go for the default local setup — no API key required when USE_API_KEYS=false (the OSS default). If your instance enables auth (USE_API_KEYS=true or hosted/multi-tenant), append "--header", "x-api-key:<your-key>" to the args array. The Configuration → Connect Your Agent page generates the snippet for you and adds the header automatically when you paste your key.

Requires Node.js on your PATH (brew install node). For a stdio alternative without Docker, or full Claude Desktop / Claude Code / Cursor walkthroughs, see Configuring Claude.

CLI

brew tap datris/tap
brew install datris
datris ingest data.csv --dest postgres
datris ingest sales.csv --ai-validate "prices > 0" --ai-transform "convert dates to YYYY/MM/DD"
datris query "SELECT * FROM sales"
datris search "quarterly revenue" --store pgvector
datris tap create "Fetch S&P 500 daily prices from yfinance" --pipeline stocks
datris taps

What It Does

Source (File Upload / MinIO Event / Database Pull / Kafka)
  → Preprocessor (optional REST endpoint)
  → Data Quality (AI rules, header validation, schema validation)
  → Transformation (AI transformation, destination schema)
  → Destinations (in parallel):
      PostgreSQL, MongoDB, MinIO (Parquet/ORC), Kafka, ActiveMQ,
      REST Endpoint, Qdrant, Weaviate, Milvus, Chroma, pgvector
  → Notifications (ActiveMQ topic)

AI-Powered Features

Feature Description
MCP Server 47 tools for AI agents — pipeline CRUD, upload, query, search, profiling, taps
AI Data Quality Plain English validation rules — AI generates and runs a validation script
AI Transformation Plain English transformations — AI generates and runs a transformation script
AI Schema Generation Upload a file, get a complete pipeline config
AI Data Profiling Upload a file, get statistics + suggested validation rules
AI Error Explanation Job failures explained in plain English
Natural Language Query Ask questions in English, get SQL results
RAG Pipeline Chunk, embed, and search across 5 vector databases

Supported Formats

CSV, JSON, XML, Excel, PDF, Word (DOCX), plain text

AI Providers

Anthropic Claude (Opus 4.8 default for chat and CodeGen) · OpenAI (GPT-5.5) · Ollama (local models, optional). Embeddings via OpenAI text-embedding-3-small (recommended when you have an OpenAI key), the bundled TEI sidecar (BAAI/bge-m3 — fully local, no API key), or Ollama.

Architecture

Service Purpose
MinIO S3-compatible object store for file staging and data output
PostgreSQL Default structured destination, also hosts pgvector for RAG
MongoDB Configuration store, job status tracking, metadata
ActiveMQ File notification queue, pipeline event notifications
HashiCorp Vault Secrets management (database credentials, API keys)
TEI Text Embeddings Inference sidecar (BAAI/bge-m3) — local vector embeddings when you're not using OpenAI embeddings
Apache Kafka Optional streaming source and destination
Apache Spark Local Spark for writing Parquet/ORC to MinIO

Documentation

Full documentation at docs.datris.ai or locally at docs/.

License

AGPL-3.0

from github.com/datris/datris-platform-oss

Installing Datris

This server has no published package — it is built from source. Open the repository and follow its README.

▸ github.com/datris/datris-platform-oss

FAQ

Is Datris MCP free?

Yes, Datris MCP is free — one-click install via Unyly at no cost.

Does Datris need an API key?

No, Datris runs without API keys or environment variables.

Is Datris hosted or self-hosted?

Self-hosted: the server runs locally on your machine via the install command above.

How do I install Datris in Claude Desktop, Claude Code or Cursor?

Open Datris on unyly.org, pick your client tab (Claude Desktop, Claude Code, Cursor) and press Install — the config is generated automatically, no JSON editing.

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