Datris

Name: Datris
Author: datris

by datrisversion 1.57.0i★ –iRuns on your computer

Registry nameio.github.datris/datris

Install

Overview

Description from the maker (original English)

The data control plane for AI agents — acquire, validate, land, and query data over MCP.

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
UsageiNot measured yet★ –

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Connection: Runs on your computer
Transport: stdio, SSE
Category: Other
You need to fill in: DATRIS_API_URL, DATRIS_API_KEY

Install

Pick the AI app you use, then copy the settings.

Before you install

Basic checks: 1 of 1 passed
Runs on your computer with your user’s permissions (files, network, apps).
You fill in yourself: DATRIS_API_URL, DATRIS_API_KEY

Values marked secret are keys to your account. Never share them or paste them into a chat.

You need

PyPIPython’s uv installed (for the uvx command)

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "datris": {
      "command": "uvx",
      "args": [
        "datris-mcp-server"
      ],
      "env": {
        "DATRIS_API_URL": "your-value",
        "DATRIS_API_KEY": "your-value"
      }
    }
  }
}

Run this in your terminal

claude mcp add datris --env DATRIS_API_URL=your-value --env DATRIS_API_KEY=your-value -- uvx datris-mcp-server

Fill in the empty values before running it.

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "datris": {
      "command": "uvx",
      "args": [
        "datris-mcp-server"
      ],
      "env": {
        "DATRIS_API_URL": "your-value",
        "DATRIS_API_KEY": "your-value"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "datris": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "datris-mcp-server"
      ],
      "env": {
        "DATRIS_API_URL": "your-value",
        "DATRIS_API_KEY": "your-value"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

Values you fill in

DATRIS_API_URL required env
URL of the Datris server API (default http://localhost:8080)
DATRIS_API_KEY secret env
API key for the Datris server (only when USE_API_KEYS=true)

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 6, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account datris, which owns the repo
… Not checked yet
Still maintained
We have not read this repo from GitHub yet
… Not checked yet
Has a licence
We have not read this repo from GitHub yet
– Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
– Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
… Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Otheri

More stars than 0% of the other 32403 servers in this category
Published to the registry more recently than 98% of the other 32403 servers in this category
Basic checks equal to or better than 100% of the other 32403 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is Datris safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

You need to fill in: DATRIS_API_URL, DATRIS_API_KEY

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (1.57.0) reached the registry yesterday.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv), Docker on your computer.

What can I use instead?

In the same category: Spec Driven Development, Remote Desktop Commander, LickityClick

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README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

Datris — The Data Control Plane for AI Agents

datris.ai · Documentation · MCP Registry · PyPI

⭐ If Datris is useful to you, star the repo — it helps other people find it.

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.

See it in 90 seconds — describe the data you need, and the Assistant builds the tap, runs it, and lands the data:

https://github.com/user-attachments/assets/4f03dc12-e9d7-4f93-8ba4-d712ab7d3733

🔊 Sound is off by default — click the speaker icon in the player.

Why Datris?

https://github.com/user-attachments/assets/ba3de886-1413-4d92-92bb-99018cfaff38

🔊 Sound is off by default — click the speaker icon in the player, or watch on YouTube.

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 — 88 tools behind a single MCP server. Claude, Cursor, and any MCP-compatible agent learn one interface instead of 88 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
Scoped keys, approval gates, audit log — each agent gets its own API key limited to the capabilities you grant; an agent policy decides per action whether an agent runs it, waits for a person to approve, or is refused; an audit log, once you turn it on (USE_AUDIT_LOG=true), records who did what, best effort by default and without loss in strict mode
Every run recorded — job state and row counts for every run, provenance on its rows with stamping on; 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 once the audit log is on (see strict mode)
Self-host anywhere — on-prem, any cloud, or your laptop; 100% open-source infrastructure (MinIO, PostgreSQL, MongoDB, Kafka, Vault), Apache-2.0, no managed service

Quick Start

You need Docker and one AI provider key (Anthropic, OpenAI, Grok, Azure OpenAI, or Amazon Bedrock). This pulls pre-built images and runtime files, seeds a .env, and starts the stack into ./datris — no git checkout required. The installer asks for the key:

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

Installing from a coding agent or a CI job with no terminal attached? Export the key first (ANTHROPIC_API_KEY, OPENAI_API_KEY, or another provider's): the installer asks nothing and reads it from the environment. Then check http://localhost:8080/api/v1/version answers before going on. Full steps: Install for Agents.

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

# 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 (or the AZURE_OPENAI_* trio, XAI_API_KEY, or AI_PROVIDER=bedrock) 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 multi-tenant), append "--header", "x-api-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.

To teach the coding agents in your own projects (Claude Code, Codex, and similar) to use Datris for data work, install the datris-platform skill from skills/ — see Agent Skill.

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 --collection financial_docs datris tap create "Fetch S&P 500 daily prices from yfinance" --pipeline stocks datris taps datris doctor # operational self-check; prints the fix for every finding

What It Does

Source (Tap / File Upload / MinIO Event / Database Pull / Kafka)

→ Preprocessor (optional REST endpoint) → Field Protection (pseudonymize, mask, redact, encrypt, or drop sensitive fields) → Data Quality (AI rules, header validation, schema validation) → Transformation (AI transformation, destination schema) → Destinations (in parallel): PostgreSQL, MongoDB, Snowflake, Databricks, Microsoft SQL Server / Azure SQL, MinIO / S3 (Parquet, ORC, Apache Iceberg), Kafka, ActiveMQ, REST Endpoint, Qdrant, Weaviate, Milvus, Chroma, pgvector → Notifications (ActiveMQ topic)

AI-Powered Features

Feature	Description
MCP Server	88 tools for AI agents — pipeline CRUD, upload, query, search, profiling, taps, catalogs, provenance
Taps	Describe a source in plain English — AI generates a Python script that fetches it on demand or on a schedule, run in an isolated container
Assistant	Conversational tap and pipeline creation, operations, and configuration in the UI
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
Field Protection	Pseudonymize, mask, redact, encrypt, or drop sensitive fields before any AI stage or destination sees them

Supported Formats

CSV, JSON, XML, Excel, PDF, Word (DOCX), PowerPoint, HTML, email, EPUB, plain text

AI Providers

Anthropic Claude (Opus 5.5 default for chat and CodeGen) · OpenAI (GPT-5.6 Sol default for chat and CodeGen) · Azure OpenAI (bring your Azure resource; models by deployment name) · Amazon Bedrock (Claude through your AWS account — IAM auth, AWS billing, IAM-role support with zero stored keys) · Grok (xAI's models through their OpenAI-compatible API) · Ollama (local models, optional). Embeddings via OpenAI text-embedding-3-small (recommended when you have an OpenAI key), Azure OpenAI, 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, and Iceberg tables to MinIO / S3
Tap runner	Isolated container that runs AI-generated tap scripts with no platform credentials and no route to internal services
MCP server	Bundled MCP endpoint (SSE on port 3000) for AI agents

Documentation

Full documentation at docs.datris.ai or locally at docs/. Going to production? Start with Security Architecture and SECURITY.md.

License

Apache License 2.0. See NOTICE for attribution.

Datris

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

Values you fill in

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Otheri

Common questions

Alternatives

Spec Driven Development

Remote Desktop Commander

LickityClick

README from the maker

Datris — The Data Control Plane for AI Agents

Why Datris?

Quick Start

Connect an AI Agent

CLI

What It Does

AI-Powered Features

Supported Formats

AI Providers

Architecture

Documentation

License