Keboola MCP Server
Keboola's open-source MCP Server lets Claude, Cursor, or ChatGPT build governed Keboola data pipelines in plain language.
Pick Keboola MCP Server if Keboola is already your platform — the AI layer is included in the Free tier at $0 and turns Claude or Cursor into a governed pipeline console with audit and lineage attached. Skip it if you only want an AI helper bolted onto Snowflake or BigQuery with no platform commitment, because there is no Keboola project for the agent to drive. The real budget question isn't the MCP Server, it's compute: 120 free minutes in month one, then 60 minutes/month, then top-ups at $0.14 per minute. Serious agent-driven workloads push you toward Enterprise at contact-us pricing rather than a self-serve middle tier.
Verified 3d ago · liveness 74/100 · cite: rightaichoice.com/tools/keboola-mcp-server
- Data engineers who want to prototype governed pipelines in natural language instead of wiring connectors by hand
- Analysts who need to ship a working pipeline without waiting on the engineering backlog
- Consultancies and agencies running client data work where POC-to-delivery time is the margin
- Teams already on Keboola that want their AI assistant to drive storage, transformations, and jobs
- Teams wanting a standalone chat product with no data platform behind it — the agent needs something to drive
- Anyone without an existing Keboola project
- Organizations with no cloud warehouse such as Snowflake, BigQuery, or DuckDB as a backend
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Skip Keboola MCP Server if you don't already run a Keboola project and a cloud warehouse — the MCP Server has nothing to drive without them, and there is no standalone chat UI.
The Free tier's compute allowance is 120 minutes in your first month and then 60 minutes/month, so agent-driven pipeline building past that is metered.
Free at $0 is genuinely usable for prototyping: unlimited ETL/ELT pipelines, 1,500+ data sources, SQL and Python transformations, Flow Builder, MCP Server included, plus an extra-small Snowflake backend and 120 compute minutes in month one then 60/month. That undercuts most governed-pipeline stacks where you pay for the warehouse and the orchestration separately. But the moment agent-driven compute becomes routine, the $0.14/minute top-ups blur the price advantage, and the next published tier
In short
Keboola MCP Server — Keboola's open-source MCP Server lets Claude, Cursor, or ChatGPT build governed Keboola data pipelines in plain language. Best for Data engineers who want to prototype governed pipelines in natural language instead of wiring connectors by hand, Analysts who need to ship a working pipeline without waiting on the engineering backlog, Consultancies and agencies running client data work where POC-to-delivery time is the margin. Free to use.
What people actually say about Keboola MCP Server — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
27 mentions across 2 sources (YouTube, Product Hunt) · researched Jul 5, 2026.
Average across the 2 sources that answered — each source counts once, not each post.
- +Build production-grade data pipelines with a single natural language prompt.
- +Integrates with Claude, Cursor, and ChatGPT via MCP for AI agent control.
- +700+ data connectors for ETL/ELT from diverse sources.
- +Enterprise-grade governance: SOC 2, HIPAA, GDPR compliance out of the box.
- +Auto-deploys agents and configs in under a minute, say early users.
- −Community feedback is limited to launch hype; long-term reliability unproven.
- −Free compute minutes are low after the first month (60 min/month).
- −Non-engineers may need tailored prompts for marketing or finance use cases.
- −Vendor lock-in to Keboola platform makes migration difficult.
- −No independent reviews on Reddit, Hacker News, or App Store yet.
- • Compute overages if Free tier minutes are exceeded
- • Enterprise pricing is custom and may be costly for small teams
Viability Score
How well maintained and how widely used is Keboola MCP Server? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: October 2026
How we score →Key Features
- Natural-language pipeline generation through MCP in Claude, Cursor, and ChatGPT
- Open-source bridge exposing Keboola storage, transformations, and jobs as AI-accessible tools
- Governed transformations and pipelines shipped without hand-wiring connectors or glue code
- Root-cause analysis for failed jobs in plain language with suggested fixes and replay
- Zero-effort documentation with lineage, schema changes, and pipeline context
- Data quality checks for uniqueness, completeness, and thresholds enforced in-pipeline
- 1,500+ data sources for ETL/ELT
- SQL, Python, and R transformations
- Flow Builder visual pipeline builder
- Data Streams for real-time data ingestion
- Change Data Capture (CDC) for databases
- Active Metadata management and Data Catalog
- Activity Center monitoring and logging
- OpenTelemetry OTLP endpoint into Storage
- CLI for managing Keboola from your terminal
About Keboola MCP Server
Keboola MCP Server is Keboola's open-source implementation of Anthropic's Model Context Protocol. It connects MCP-compatible AI assistants and IDEs — Claude, Cursor, ChatGPT — to a Keboola project so that describing a workflow in natural language produces a governed, production-ready pipeline instead of a throwaway script. Keboola storage, transformations, and jobs are exposed as AI-accessible tools, which means no glue code between your assistant and your data infrastructure. Four documented workflows cover the daily grind: pipeline creation (describe it in Cursor, Keboola makes it governed and production-ready), job debugging (ask why a job failed, get root-cause analysis with suggested fixes, approve and replay), automatic documentation (every pipeline documented with lineage, schema changes, and context), and data quality assessment (uniqueness, completeness, and threshold checks enforced in-pipeline before bad data reaches downstream tables). Setup is three steps: create your Keboola project, connect via MCP to your assistant or IDE, then build. Keboola advertises operation up to 10× faster with the MCP Server. It is built for developers, analysts, and data engineers who already have a Keboola project and a cloud warehouse underneath (Snowflake, BigQuery, DuckDB, or similar). The MCP Server carries no separate fee — it is listed inside the Free tier, which includes unlimited ETL/ELT pipelines, access to 1,500+ data sources, SQL and Python transformations, Flow Builder, and an extra-small Snowflake backend. Enterprise adds Data Share/Catalog, Data Applications and workflow templates, tailored CDC and streaming, Dev/Prod mode with Git CI/CD and SOX, VPC deployment, SOC 2 Type II, GDPR/HIPAA, and any storage backend. A DIY MCP wrapper gives you the connection but not the governance layer; here audit, lineage, and observability ride along with the agent.
Behind the Verdict
Keboola MCP Server is best understood as the AI control surface on top of Keboola's data platform, not a standalone tool. The value is what the agent can reach: storage, transformations, and jobs exposed as MCP tools, which means an assistant in Cursor or Claude can create governed pipelines, diagnose failed jobs in plain language, generate documentation with lineage, and enforce uniqueness/completeness/threshold checks in-pipeline — all without the analyst hand-wiring connectors. The governance layer is the differentiator versus a DIY MCP wrapper: audit, lineage, and observability are attached to whatever the agent builds, so the output is a production workflow rather than a script. Strengths: four documented workflows that map to real daily tasks rather than demo scenarios; 1,500+ data sources and support for SQL, Python, and R transformations; Flow Builder for visual pipelines plus Data Streams for real-time ingestion and CDC for databases; open-source and included in the Free tier at $0 with no separate MCP Server fee. Weaknesses: it requires an existing Keboola project and a cloud warehouse underneath — there is no standalone chat UI, the assistant is whichever MCP-compatible client you use; the Free tier's compute envelope (120 minutes in month one, 60 minutes/month, then $0.14/minute top-ups) is a prototyping allowance, so agent-driven work at volume becomes metered; and above Free the next published step is Enterprise at contact-us pricing, which is a sales conversation. Keboola's own positioning leans toward finance consolidation, group reporting, and marketing analytics, so general-purpose data science is not the focus. Where it fits: teams already on Keboola, consultancies running client data work where POC-to-delivery time is the margin, and developers wiring AI agents into enterprise data infrastructure who need audit and lineage to survive review.
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Real-world workflow fit
Concrete scenarios for the personas Keboola MCP Server actually fits — and what changes day-one when you adopt it.
You open Cursor, connect to Keboola via the MCP Server, and describe the pipeline you want: ingest from Salesforce, transform in SQL, land in the Snowflake backend, with completeness checks on the customer ID column.
Outcome: Keboola builds the pipeline as a governed workflow with lineage and audit attached — no connector wiring by hand, and the quality check runs in-pipeline before data lands downstream.
A scheduled job fails overnight. You ask your assistant why it failed rather than digging through logs.
Outcome: You get root-cause analysis in plain language with suggested fixes; you approve, replay, and move on, while Keboola keeps the audit trail.
You stand up a new client POC by creating a Keboola project, connecting the MCP Server to Claude, and describing the pipelines the client needs.
Outcome: POC-to-delivery compresses because the pipelines arrive documented with schema changes and context, and the client gets governance rather than a one-off script.
Use Cases
- Describe a data pipeline in natural language in Cursor and have Keboola build and deploy it as a governed workflow.
- Ask your assistant why a job failed and get plain-language root-cause analysis with suggested fixes to approve and replay.
- Generate automatic pipeline documentation covering lineage, schema changes, and context instead of maintaining stale wiki pages.
- Enforce uniqueness, completeness, and threshold checks by describing them, so bad data is flagged before it reaches downstream tables.
- Consolidate multiple ERPs into one governed data warehouse foundation for finance and AI readiness.
- Run governed AI agents for finance tasks such as audit and controlling.
- Manage Keboola projects from an IDE or terminal while keeping audit and reliability in place.
Models Under the Hood
as of 2026-10-18
Limitations
- The MCP Server is an integration layer, not a product on its own: it requires an existing Keboola project and a cloud data warehouse (Snowflake, BigQuery, DuckDB, or similar) to do anything.
- There is no standalone chat interface — the assistant you drive is Claude, Cursor, or ChatGPT.
- The Free plan is effectively a prototyping tier: 120 compute minutes in the first month, then 60 minutes/month, with top-ups at $0.14 per minute, so heavier agent-driven work becomes a metered cost.
- Above Free the next published step is Enterprise at contact-us pricing, which is a sales conversation rather than a click.
- Keboola's own positioning leans toward finance consolidation, group reporting, and marketing analytics, so general-purpose data science needs are not the focus.
as of 2026-10-09
Verification history
We have re-verified Keboola MCP Server 10 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-checked, vendor evidence unchanged
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-checked, vendor evidence unchanged
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Showing the 6 most recent of 10 verification passes.
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published Keboola MCP Server tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Free
$0
Ideal for
Developers, analysts, and data engineers prototyping governed pipelines on a single Keboola project — and the only tier that includes the MCP Server without a sales conversation.
What this tier adds
Starting tier at $0: unlimited ETL/ELT pipelines, 1,500+ data sources, SQL and Python transformations, Flow Builder, MCP Server, extra-small Snowflake backend, 120 compute minutes in month one then 60/month.
Enterprise
Custom
Ideal for
Groups and enterprises running finance consolidation, group reporting, or multi-project data work that needs governance certification, SSO, and VPC deployment.
What this tier adds
Adds Data Share/Catalog, Data Applications and workflow templates, tailored CDC and streaming, Dev/Prod mode with Git CI/CD and SOX, Public/Private SaaS deployments (VPC), SOC 2 Type II with GDPR/HIPAA, SAML and Google/Azure SSO, any storage backend, and a dedicated TAM.
Where the pricing makes sense
The company stage and team size where Keboola MCP Server's pricing actually pencils out — and where peers do it cheaper.
Free at $0 is genuinely usable for prototyping: unlimited ETL/ELT pipelines, 1,500+ data sources, SQL and Python transformations, Flow Builder, MCP Server included, plus an extra-small Snowflake backend and 120 compute minutes in month one then 60/month. That undercuts most governed-pipeline stacks where you pay for the warehouse and the orchestration separately. But the moment agent-driven compute becomes routine, the $0.14/minute top-ups blur the price advantage, and the next published tier
Setup time & first value
How long it actually takes to get something useful out of Keboola MCP Server — broken out by persona, not the marketing-page minute.
For a developer who already has a Keboola project and an MCP-compatible assistant, first value lands in minutes: three steps — create the project, connect via MCP, describe a pipeline. Customer quotes cited by Keboola put a Claude setup at around 10 minutes. Budget more time if you're standing up a new Keboola project or warehouse from scratch, since the MCP layer only kicks in once a project
Switching to or from Keboola MCP Server
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From a DIY MCP wrapper: point your MCP client at the Keboola MCP Server and your agent gains storage, transformation, and job tools backed by Keboola governance instead of raw connections.
- ↗To a hand-rolled MCP wrapper on Snowflake or BigQuery: you keep the connection but lose the governance, lineage, and audit layer Keboola attaches to each workflow.
Integrations
Resources & Guides
Tutorials & Learning
How to Connect the Keboola MCP Server with Claude Desktop
Keboola
How to Connect the Keboola MCP Server with Cursor AI
Keboola
How to Connect the Keboola MCP Server with Claude AI
Keboola
YouTube returned 6 videos for “Keboola MCP Server”, and we withheld 1: 1 did not mention Keboola MCP Server. Showing the 5 we can prove are about Keboola MCP Server.
Official links
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