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Datris MCP Server

Datris — The First AI Agent-Native Data Platform

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

Related MCP server: aegis-dq

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.

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
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 (Sonnet 4.6 default, Opus 4.8 for CodeGen) · OpenAI (GPT-5.5) · Ollama (local models, optional). Embeddings via TEI sidecar (BAAI/bge-m3) when using Anthropic, or text-embedding-3-small when using OpenAI.

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) for vector embeddings without an OpenAI key

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

A
license - permissive license
-
quality - not tested
D
maintenance

Maintenance

Maintainers
Response time
Release cycle
Releases (12mo)
Commit activity

Latest Blog Posts

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