Dataiku MCP Server
Provides tools for interacting with Dataiku DSS REST APIs, including project, dataset, recipe, job, scenario, managed folder, variable, connection, and code environment management.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Dataiku MCP Server list all projects"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Sunset — use clssck/dataiku-sdk instead
Dataiku MCP Server is no longer actively maintained. Development has moved to the
Dataiku DSS SDK — a schema-first TypeScript SDK and
dss CLI that is more actively maintained and covers far more of the DSS API:
Broader coverage — 30+ DSS resources vs this server's narrow tool set.
A scriptable
dssCLI with a machine-readable command contract for agents.One-command agent skill install for Claude, Codex, Cursor, Pi, and OMP.
Migrate here → https://github.com/clssck/dataiku-sdk
Dataiku MCP Server
MCP server for Dataiku DSS REST APIs, focused on flow analysis and reliable day-to-day operations (projects, datasets, recipes, jobs, scenarios, folders, variables, connections, and code environments).
Cursor one-click install includes placeholder environment values. Update
DATAIKU_URL,DATAIKU_API_KEY, and optionallyDATAIKU_PROJECT_KEYafter adding the server.
Related MCP server: mcp-fabric-api
What You Get
Deterministic normalized flow maps (
project.map) with recipe subtypes and connectivity.Summary-first outputs with explicit raw/detail toggles where needed.
Broad test coverage (unit + live integration + optional destructive integration suite).
Strong error taxonomy in responses:
not_found,forbidden,validation,transient,unknownwith retry hints.
Tool Coverage
project:list,get,metadata,flow,mapdataset:list,get,schema,preview,metadata,download,create,update,deleterecipe:list,get,create,update,delete,downloadjob:list,get,log,build,buildAndWait,wait,abortscenario:list,run,status,get,create,update,deletemanaged_folder:list,get,contents,download,upload,delete_filevariable:get,setconnection:infercode_env:list,get
Prerequisites
Node.js 20+
npm
Dataiku DSS URL + API key
Quick Start
npm ci
npm run buildRun as a local CLI after build:
node dist/index.jsUse directly from npm (after publish):
npx -y dataiku-mcpLocal Build And Testing
Recommended local workflow from repo root:
# install deps
npm ci
# static checks
npm run check
# unit tests
npm test
# build distribution
npm run build
# run MCP server locally (dev)
npm startOptional live DSS integration tests:
# requires DATAIKU_URL, DATAIKU_API_KEY, DATAIKU_PROJECT_KEY in .env
npm run test:integration
# includes destructive actions (create/update/delete)
DATAIKU_MCP_DESTRUCTIVE_TESTS=1 npm run test:integrationRepository Layout
src/: MCP server and tool implementations.tests/: unit + integration test suites.examples/: demos, fixtures, artifacts, and ad-hoc local scripts.bin/: package executable entrypoint.dist/: compiled output (generated).
Create a local env file:
cp .env.example .env
# then edit .envRun directly in dev:
npm startExample scripts and sample outputs are kept under examples/ to avoid root-level clutter.
Environment Variables
DATAIKU_URL: DSS base URLDATAIKU_API_KEY: DSS API keyDATAIKU_PROJECT_KEY(optional): default project keyDATAIKU_REQUEST_TIMEOUT_MS(optional): per-attempt request timeout in milliseconds (default:30000)DATAIKU_RETRY_MAX_ATTEMPTS(optional): max attempts for retry-enabled requests (GETonly, default:4, cap:10)DATAIKU_DEBUG_LATENCY(optional): set to1/trueto include per-tool timing diagnostics instructuredContent.debug.latency(off by default)
MCP Client Setup Guide
Use this server command in clients (npm package):
{
"command": "npx",
"args": ["-y", "dataiku-mcp"],
"env": {
"DATAIKU_URL": "https://your-dss-instance.app.dataiku.io",
"DATAIKU_API_KEY": "your_api_key",
"DATAIKU_PROJECT_KEY": "YOUR_PROJECT_KEY"
}
}Windows note: if your MCP client launches commands without a shell, use npx.cmd:
{
"command": "npx.cmd",
"args": ["-y", "dataiku-mcp"],
"env": {
"DATAIKU_URL": "https://your-dss-instance.app.dataiku.io",
"DATAIKU_API_KEY": "your_api_key",
"DATAIKU_PROJECT_KEY": "YOUR_PROJECT_KEY"
}
}You can also run TypeScript directly during development:
{
"command": "npx",
"args": ["tsx", "/absolute/path/to/Dataiku_MCP/src/index.ts"],
"env": {
"DATAIKU_URL": "https://your-dss-instance.app.dataiku.io",
"DATAIKU_API_KEY": "your_api_key",
"DATAIKU_PROJECT_KEY": "YOUR_PROJECT_KEY"
}
}Claude Desktop
Open Claude Desktop ->
Settings->Developer->Edit Config.Add this under
mcpServersinclaude_desktop_config.json:
{
"mcpServers": {
"dataiku": {
"command": "npx",
"args": ["-y", "dataiku-mcp"],
"env": {
"DATAIKU_URL": "https://your-dss-instance.app.dataiku.io",
"DATAIKU_API_KEY": "your_api_key",
"DATAIKU_PROJECT_KEY": "YOUR_PROJECT_KEY"
}
}
}
}Cursor
Cursor supports both project-scoped and global MCP config:
Project:
.cursor/mcp.jsonGlobal:
~/.cursor/mcp.json
Example:
{
"mcpServers": {
"dataiku": {
"command": "npx",
"args": ["-y", "dataiku-mcp"],
"env": {
"DATAIKU_URL": "https://your-dss-instance.app.dataiku.io",
"DATAIKU_API_KEY": "your_api_key",
"DATAIKU_PROJECT_KEY": "YOUR_PROJECT_KEY"
}
}
}
}Cline (VS Code extension)
Open Cline -> MCP Servers -> Configure MCP Servers.
Add this server block in
cline_mcp_settings.json:
{
"mcpServers": {
"dataiku": {
"command": "npx",
"args": ["-y", "dataiku-mcp"],
"env": {
"DATAIKU_URL": "https://your-dss-instance.app.dataiku.io",
"DATAIKU_API_KEY": "your_api_key",
"DATAIKU_PROJECT_KEY": "YOUR_PROJECT_KEY"
}
}
}
}Codex / project-level MCP config
This repo already includes a project-scoped MCP file at .mcp.json.
The checked-in .mcp.json uses node node_modules/tsx/dist/cli.mjs src/index.ts for cross-platform startup (including Windows); run npm ci first.
NPM Release Workflow
This repo includes a manual GitHub Actions release workflow:
Workflow file:
.github/workflows/release.ymlTrigger:
Actions->Release NPM Package->Run workflow
Inputs:
bump:patch | minor | majorversion: optional exact version (overridesbump)publish: whether to publish to npm
Required repository configuration:
GitHub variable:
NPM_RELEASE_ENABLED=trueOptional variable:
NPM_PUBLISH_ACCESS=publicTrusted publisher configured on npmjs.com for this package/repo/workflow
The workflow will:
Install dependencies, run checks/tests, and build.
Bump package version and create git tag.
Push commit + tag to
main.Publish to npm with GitHub OIDC trusted publishing (if
publish=true).Create a GitHub Release with generated notes.
Trusted publishing setup (npm):
Open
https://www.npmjs.com/package/dataiku-mcp->Settings->Trusted Publisher.Choose
GitHub Actions.Set:
Organization or user:
clssckRepository:
Dataiku_MCPWorkflow filename:
release.yml
Save.
Official MCP Registry
This repo is configured for MCP Registry publishing:
Metadata file:
server.jsonWorkflow:
.github/workflows/publish-mcp-registry.ymlRequired package field:
mcpNameinpackage.json
Server namespace:
io.github.clssck/dataiku-mcp
Publish paths:
Manual: run
Publish to MCP Registryin GitHub Actions.Automatic: run the npm release workflow with
publish=true(it triggers MCP Registry publish).
Validation notes:
server.json.namemust matchpackage.json.mcpName.server.json.packages[].identifier+versionmust reference a real npm publish.
Recommended Verification Prompt
After adding the server in a client, run:
projectwith{ "action": "map", "projectKey": "YOUR_PROJECT_KEY" }(defaults tomaxNodes=300,maxEdges=600; override as needed)
You should receive a flow summary in text and normalized nodes, edges, stats, roots, and leaves under structuredContent.map.
When truncation limits are applied (default maxNodes=300, maxEdges=600), structuredContent.truncation reports before/after node+edge counts and whether truncation occurred.
Notes
project.mapreturns a compact text summary; full normalized graph is instructuredContent.map.Arrays in normalized map output are deterministically sorted to reduce diff churn.
job.waitandjob.buildAndWaitincludestructuredContent.normalizedStatewith one ofterminalSuccess | terminalFailure | timeout | nonTerminalwhile preserving raw DSSstate.With
DATAIKU_DEBUG_LATENCY=1, responses include per-tool and per-API-call latency metrics understructuredContent.debug.latency.List-style responses are token-bounded by default; use
limit/offset(and action-specific caps likemaxNodes,maxEdges,maxKeys,maxPackages) to page or expand results when needed.dataset.getandjob.getare summary-first by default; passincludeDefinition=trueto include full DSS JSON instructuredContent.definition.
Sources
MCP local server connection docs: https://modelcontextprotocol.io/docs/develop/connect-local-servers
Cursor MCP docs: https://cursor.com/docs/context/mcp
Cline MCP docs: https://docs.cline.bot/mcp/configuring-mcp-servers
Available Tools
9 toolscode_envB
Code env ops: list/get. get returns package summaries; set full=true for full package lists.
Inputs: no named parameters.
connectionA
Connection discovery (action: infer). Default mode=fast uses DSS connection names; mode=rich scans project datasets for inferred type/schema/managed details.
Inputs: no named parameters.
datasetB
Dataset ops: list/get/schema/preview/metadata/download/create/update/delete. get is summary-first; set includeDefinition=true to include full JSON definition.
Inputs: no named parameters.
jobB
Job ops: list/get/log/build/buildAndWait/wait/abort. get is summary-first; set includeDefinition=true to include full JSON definition.
Inputs: no named parameters.
managed_folderC
Managed folder ops: list/get/contents/download/upload/delete_file.
Inputs: no named parameters.
projectA
Project ops: list/get/metadata/flow/map. map returns normalized connectivity in structuredContent.map; includeRaw adds original graph payload.
Inputs: no named parameters.
recipeC
Recipe ops: list/get/create/update/delete/download. get is summary-first; set includePayload=true to include payload snippets.
Inputs: no named parameters.
scenarioA
Scenario ops: list/run/status/get/create/update/delete. get is summary-first; use includeScript/includeDefinition for details.
Inputs: no named parameters.
variableB
Project variable ops: get/set. set merges provided standard/local keys with existing values.
Inputs: no named parameters.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
TDQS
Scored across 9 tools
Each tool maps to a distinct Dataiku resource type (project, dataset, recipe, job, scenario, variable, managed_folder, connection, code_env), with no overlapping responsibilities. The descriptions clearly differentiate the scope of each tool.
All tool names are single lowercase nouns or snake_case compound nouns (e.g., managed_folder, code_env), following a predictable resource-based pattern. No mixing of styles or vague verbs.
With 9 tools, the server is well-scoped for Dataiku's domain. Each entity earns its place and the count is comfortably within the ideal 3-15 range.
The core workflows (list/inspect datasets, recipes, jobs, scenarios; run builds and scenarios) are well covered. Some gaps exist, such as project creation/deletion and code_env management, but these are secondary for common agent tasks.
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