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Documentation Index

Fetch the complete documentation index at: https://docs.datris.ai/llms.txt

Use this file to discover all available pages before exploring further.

This page covers the client-side configuration for Anthropic’s Claude products to talk to the Datris MCP server. For server-side details (architecture, transports, available tools), see MCP Server (AI Agent Integration).

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Configuring Claude Desktop

Edit ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows), then quit and relaunch Claude Desktop. If you’re running the standard Docker stack, route Claude Desktop through the dockerized MCP server on port 3000 using mcp-remote: 
{
    "mcpServers": {
        "datris": {
            "command": "npx",
            "args": ["-y", "mcp-remote", "http://localhost:3000/sse", "--transport", "sse-only"]
        }
    }
}
Why this is the default: connections via SSE register MCP sessions with the Datris server, so Claude Desktop appears in the Agents tab of the Datris UI with live tool-call streaming. The stdio alternative below makes plain REST calls and is invisible to that monitor. Requires Node.js (for npx).

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Alternative: stdio via uvx (no Docker)

If you’re not running the Docker stack — for example, you only want the MCP server and run the Datris API separately — use the PyPI package directly: 
{
    "mcpServers": {
        "datris": {
            "command": "uvx",
            "args": ["datris-mcp-server"],
            "env": {
                "PIPELINE_URL": "http://localhost:8080"
            }
        }
    }
}
Requires uv / uvx (brew install uv). On macOS, Claude Desktop is a GUI app and doesn’t inherit your shell PATH, so use the full path to uvx (e.g. /Users/you/.local/bin/uvx) if uvx alone doesn’t resolve. Tool calls will not appear in the Agents tab.

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Configuring Claude Code

Add to .mcp.json in your project root. The same two options apply. 
{
    "mcpServers": {
        "datris": {
            "command": "npx",
            "args": ["-y", "mcp-remote", "http://localhost:3000/sse", "--transport", "sse-only"]
        }
    }
}

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Alternative: stdio via uvx (no Docker)

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

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Talking to Claude

Once the MCP server is configured, just talk to Claude naturally. Claude sees all 47 Datris tools and picks the right one based on your request.

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First prompts (empty Datris)

If you’ve just installed Datris and have no data yet, these prompts exercise the full create → query loop without needing files on hand. Watch the Agents tab in the Datris UI as you go — you’ll see each tool call stream in real time. 1. Confirm discovery works (returns empty, but proves the round-trip):
“List my Datris pipelines and tell me what’s there.”
2. Have Claude generate sample data and ingest it:
“Generate 50 rows of sample stock price data (symbol, date, open, close, volume) and ingest it into PostgreSQL as a pipeline called stock_prices.”
This single prompt exercises create_pipeline (with destination schema), the actual ingest, and several supporting tools — multiple tool calls light up the activity log. 3. Query the data you just created:
“Show me the top 10 rows in stock_prices by volume.”
4. Add AI-generated data quality rules:
“Profile the stock_prices pipeline and suggest data quality rules I should add.”
This exercises the AI/CodeGen path — Claude writes Python validators, Datris runs them locally. 5. Read the pipeline configuration resource:
“Read the Datris pipeline configuration reference and summarize what destination types are supported.”
Confirms Claude can pull MCP resources (not just tools) — useful to verify your client supports both. For the RAG path, ingest the document outside the chat (see the warning below), then ask Claude to search it: 
# from your terminal
datris ingest annual-report.pdf --dest pgvector --pipeline reports
“Search the reports pipeline for [topic from the PDF].”

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Ingest data

“Create a pipeline called stock_prices and ingest this CSV into PostgreSQL”
“Ingest this sales data into PostgreSQL and validate that all prices are positive”
Don’t drag large local files into the chat to ingest them. When you say “ingest this PDF” with a file attached, Claude has to base64-encode the bytes and pass them as a string argument to the upload_data tool — that string traverses the conversation context, and a 300KB PDF alone consumes ~90K tokens before any work happens. Conversations hit “too long to continue” almost immediately.Use the right tool for each shape of input:
  • Local files of any size — use the CLI: datris ingest <file> --dest <destination>. No LLM, no context cost.
  • Files at a URL (S3, MinIO, HTTPS) — ask Claude to create a Datris tap that fetches them. Bytes are pulled server-side; the agent only handles the URL.
  • Small inline data (a few rows of CSV, a JSON snippet, generated sample data) — fine to drop into the chat.
The chat path is for natural-language operations (querying, profiling, pipeline management, search), not for funneling local-disk files through a remote conversation.

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Query data

“Show me the top 10 stocks by volume from the stock_prices table”
“What’s the average closing price for AAPL?”
“How many orders were placed in March?”

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Search documents (RAG)

“Search my financial documents for information about quarterly revenue”
“What does the employee handbook say about the return policy?”

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Profile data

“Profile this CSV file and tell me what validation rules I should add”
“What does this data look like? Give me a summary”

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Manage pipelines

“List all my pipelines”
“Delete the test_pipeline”
“What’s the status of the stock_prices pipeline?”

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Explore databases

“What tables are in my PostgreSQL database?”
“Show me the columns in the orders table”
“What vector collections do I have?”

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Health and diagnostics

“Are all the backend services running?”
“Check the health of the platform”
You can drag small inline files (a CSV with tens of rows, a short JSON snippet) directly into the chat for ingestion or profiling. For larger or binary files, use the CLI or a tap (see the warning above).