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AI Agent-Native Data Platform — ingest, validate, transform, and query data.
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AI Agent-Native Data Platform — ingest, validate, transform, and query data.
Security Report
4.2
Use Caution4.2High RiskThe Datris MCP server is a well-intentioned data platform connector with reasonable security architecture for its purpose. Authentication is properly delegated to per-session API keys via context variables, and the server correctly avoids storing credentials. However, there are several quality and design concerns: overly broad exception handling masks errors, sensitive data (API keys, full request/response bodies) is logged to an in-memory activity buffer accessible via a snapshot function, the server makes unauthenticated HTTP calls to a local Datris API by default, and there is no input validation on user-provided parameters before forwarding to downstream APIs. These issues are not critical but should be addressed to harden the implementation. Supply chain analysis found 13 known vulnerabilities in dependencies (0 critical, 4 high severity). Package verification found 1 issue.
4 files analyzed · 23 issues found
Security scores are indicators to help you make informed decisions, not guarantees. Always review permissions before connecting any MCP server.
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What You'll Need
Set these up before or after installing:
URL of the Datris pipeline server
Environment variable: PIPELINE_URL
API key for the Datris pipeline server (if key validation is enabled)Required
Environment variable: PIPELINE_API_KEY
How to Install
Add this to your MCP configuration file:
{
"mcpServers": {
"io-github-datris-datris": {
"env": {
"PIPELINE_URL": "your-pipeline-url-here",
"PIPELINE_API_KEY": "your-pipeline-api-key-here"
},
"args": [
"datris-mcp-server"
],
"command": "uvx"
}
}
}Documentation
View on GitHubFrom the project's GitHub README.
Datris — The First AI Agent-Native Data Platform
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.shinstaller 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
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