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-1.5.6.tar.gz (22.3 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-1.5.6-py3-none-any.whl (23.1 kB view details)

Uploaded Python 3

File details

Details for the file datris_mcp_server-1.5.6.tar.gz.

File metadata

  • Download URL: datris_mcp_server-1.5.6.tar.gz
  • Upload date:
  • Size: 22.3 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-1.5.6.tar.gz
Algorithm Hash digest
SHA256 3b47d80430694565e2ddf5a54c41474ef48da9a60e527ba50ab554cc03aa5cea
MD5 fbb7a80f80dec931b5a00c3ef7aaba35
BLAKE2b-256 348294a894f707b5ea056ed041e80f9e1d3baafcd2c941d2b8999bee4df5ce7b

See more details on using hashes here.

File details

Details for the file datris_mcp_server-1.5.6-py3-none-any.whl.

File metadata

File hashes

Hashes for datris_mcp_server-1.5.6-py3-none-any.whl
Algorithm Hash digest
SHA256 ce125ce776240b70af3b1a5934d46b2323d42c1d0bdb706774b2f41436c87fd3
MD5 c38e2fb87a1901804ba3b7774f5ae6c0
BLAKE2b-256 88d8b5e358143821386523d603ccb8a9f2c9f172a4a6aa408d4a911a6902ca39

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