Skip to main content

MCP server for AI-driven data pipeline operations — ingest, validate, transform, analyze, and query data. 32 tools covering ETL, AI data quality, vector search, PostgreSQL, MongoDB, Kafka, S3/MinIO, HashiCorp Vault, Qdrant, Weaviate, Milvus, Chroma, pgvector.

Project description

Datris MCP Server

MCP (Model Context Protocol) server for the Datris AI Agent-Native Data Platform. Enables AI agents (Claude Desktop, Claude Code, Cursor, and custom frameworks) to natively interact with the platform — discover data, create pipelines, upload files, monitor jobs, search vector databases, query structured data, and answer questions with AI.

Install

pip install datris-mcp-server

Usage

stdio mode (Claude Desktop / Claude Code)

PIPELINE_URL=http://localhost:8080 datris-mcp-server

Or run directly:

PIPELINE_URL=http://localhost:8080 python server.py

SSE mode (Docker / remote agents)

PIPELINE_URL=http://localhost:8080 datris-mcp-server --sse --port 3000

Docker

The MCP server starts automatically with docker compose up in SSE mode on port 3000.

Configuration

Claude Desktop

Add to ~/Library/Application Support/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "datris": {
      "command": "datris-mcp-server",
      "env": {
        "PIPELINE_URL": "http://localhost:8080"
      }
    }
  }
}

Claude Code

Add to .mcp.json in your project root:

{
  "mcpServers": {
    "datris": {
      "command": "datris-mcp-server",
      "env": {
        "PIPELINE_URL": "http://localhost:8080"
      }
    }
  }
}

Environment Variables

Variable Default Description
PIPELINE_URL http://localhost:8080 Datris pipeline server URL
PIPELINE_API_KEY (empty) API key if pipeline has key validation enabled

Tools

30+ tools across these categories:

  • Pipeline Management — create, list, get, delete pipelines; upload files; monitor jobs
  • Vector Search — semantic search across Qdrant, Weaviate, Milvus, Chroma, pgvector
  • Database Query — read-only SQL queries (PostgreSQL) and MongoDB queries
  • Metadata Discovery — explore databases, schemas, tables, columns, collections
  • AI — RAG-powered question answering
  • System — health checks, version info

See the full documentation at docs.datris.ai/mcp-server.

License

Apache 2.0

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

datris_mcp_server-1.6.0.tar.gz (25.0 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

datris_mcp_server-1.6.0-py3-none-any.whl (25.8 kB view details)

Uploaded Python 3

File details

Details for the file datris_mcp_server-1.6.0.tar.gz.

File metadata

  • Download URL: datris_mcp_server-1.6.0.tar.gz
  • Upload date:
  • Size: 25.0 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.14.3

File hashes

Hashes for datris_mcp_server-1.6.0.tar.gz
Algorithm Hash digest
SHA256 caeef25f4c439c3fac87896473752afdaf92f3877901a9688b292b75a78c728a
MD5 1cb39dd4c9512d7df896ab1a0750caa6
BLAKE2b-256 fd863aacf757a37fe018c8a6668bcfec55f7552085bc69f8044759d4554dff80

See more details on using hashes here.

File details

Details for the file datris_mcp_server-1.6.0-py3-none-any.whl.

File metadata

File hashes

Hashes for datris_mcp_server-1.6.0-py3-none-any.whl
Algorithm Hash digest
SHA256 71400894146ef4d382dc882101fccc687af754d68b40997e28f451d8f64dbd51
MD5 412d018a835ee07a2896f3887442f12f
BLAKE2b-256 b32ab2aaf63a68e5d7531a6781778c71d4838c28cc7886fcf2ef73a6537f40eb

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page