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-1.6.9.tar.gz (28.3 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-1.6.9-py3-none-any.whl (29.1 kB view details)

Uploaded Python 3

File details

Details for the file datris_mcp_server-1.6.9.tar.gz.

File metadata

  • Download URL: datris_mcp_server-1.6.9.tar.gz
  • Upload date:
  • Size: 28.3 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-1.6.9.tar.gz
Algorithm Hash digest
SHA256 0b2b5bbe0f35accfc631b971612d08b0b729c7455ce0c36a3cc99661321a6925
MD5 482debdd4187150ccb632851c38e1d3c
BLAKE2b-256 43396b104bc8c3a4c908342dd1285f10a4fa3c2486da496d3976bbeb07605620

See more details on using hashes here.

File details

Details for the file datris_mcp_server-1.6.9-py3-none-any.whl.

File metadata

File hashes

Hashes for datris_mcp_server-1.6.9-py3-none-any.whl
Algorithm Hash digest
SHA256 f7aa3fdbc0b385afeea69081e4764584ee69e394e2ef869a5424f3a01893888e
MD5 bb449fec12d25950ef6f4c306aac0c14
BLAKE2b-256 fb2e55033d5e8ae655148e132c84994db3ad39095d47bb01037cfacf411cb4c0

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