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Datris Platform Oss MCP Server

by Datris
Developer ToolsUse Caution3.5MCP RegistryLocal
Free

Server data from the Official MCP Registry

AI Agent-Native Data Platform — ingest, validate, transform, and query data.

About

AI Agent-Native Data Platform — ingest, validate, transform, and query data.

Security Report

3.5
Use Caution3.5High Risk

The Datris MCP server is a legitimate data platform connector with reasonable architectural security patterns, but has several moderate-severity issues: environment variable leakage through HTTP headers in activity logging, insufficient input validation on external API calls, and overly permissive HTTP-based communication without proper encryption guarantees. The server's broad network access is proportionate to its purpose (orchestrating data pipelines across multiple services), but logging and credential handling need hardening. 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.

Permissions Required

This plugin requests these system permissions. Most are normal for its category.

HTTP Network Access

Connects to external APIs or services over the internet.

env_vars

Check that this permission is expected for this type of plugin.

File System Read

Reads files on your machine. Normal for tools that analyze or process local data.

File System Write

Writes or modifies files on your machine. Check that this is expected for the tool.

What You'll Need

Set these up before or after installing:

URL of the Datris pipeline serverOptional

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 GitHub

From the project's GitHub README.

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 63 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.

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 (or the AZURE_OPENAI_* trio, 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 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

FeatureDescription
MCP Server63 tools for AI agents — pipeline CRUD, upload, query, search, profiling, taps
AI Data QualityPlain English validation rules — AI generates and runs a validation script
AI TransformationPlain English transformations — AI generates and runs a transformation script
AI Schema GenerationUpload a file, get a complete pipeline config
AI Data ProfilingUpload a file, get statistics + suggested validation rules
AI Error ExplanationJob failures explained in plain English
Natural Language QueryAsk questions in English, get SQL results
RAG PipelineChunk, 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) · 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) · 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

ServicePurpose
MinIOS3-compatible object store for file staging and data output
PostgreSQLDefault structured destination, also hosts pgvector for RAG
MongoDBConfiguration store, job status tracking, metadata
ActiveMQFile notification queue, pipeline event notifications
HashiCorp VaultSecrets management (database credentials, API keys)
TEIText Embeddings Inference sidecar (BAAI/bge-m3) — local vector embeddings when you're not using OpenAI embeddings
Apache KafkaOptional streaming source and destination
Apache SparkLocal Spark for writing Parquet/ORC to MinIO

Documentation

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

License

AGPL-3.0

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Datris Platform Oss MCP Server - AI Agent-Native Data Platform — ingest, validate, | MCP Marketplace