Top data science MCP servers

Ranked by the composite AgentRank score — a weighted blend of stars (15%), freshness (25%), issue health (25%), contributors (10%), and inbound dependents (25%). Average score across all 368 data science tools is 32.5. The tools below are in the top tier.

Choosing by use case

Analytical queries and local data (DuckDB)

motherduckdb/mcp-server-motherduck is the top-scoring data science tool at 88.31. DuckDB runs in-process with zero round-trips for local data — fast analytical SQL over Parquet files, CSVs, and in-memory DataFrames. MotherDuck support extends this to cloud scale. If your agent needs to query a dataset without spinning up an external database, this is the right tool.

Jupyter notebooks

datalayer/jupyter-mcp-server scores 83.45 with 940 stars and 18 contributors. It's the canonical MCP integration for Jupyter — lets agents execute notebook cells, read cell outputs, manage kernels, and inspect variable state. If your data science workflow is notebook-first, this is the integration that closes the loop between an AI agent and interactive computation.

Big data and Spark pipelines

kubeflow/mcp-apache-spark-history-server is an official Kubeflow project, scoring 78.08. It connects agents directly to the Spark History Server — query job history, application logs, stage metrics, and executor details through natural language. If your team runs Spark jobs on Kubernetes or any cluster, this is how you give agents visibility into pipeline execution without custom tooling.

Data transformation (dbt)

dbt-labs/dbt-mcp is the official dbt MCP server from dbt Labs, scoring 72.46 with 35 contributors — the highest contributor count in this list. Agents can run dbt models, execute tests, compile SQL, and inspect lineage directly. If your data team runs dbt, this server closes the gap between the transformation layer and any LLM-driven workflow.

Web analytics (Google Analytics 4)

surendranb/google-analytics-mcp gives agents natural language access to GA4 data, scoring 68.14. Active commits through March 2026, supporting Claude, Cursor, and Windsurf. One caveat: it's a solo-contributor project, which means bus factor risk. Verify the license and consider forking for production use. If you need GA4 query access in an agent workflow, this is the only well-maintained option in the index.

Cloud data warehouses (Snowflake)

Snowflake-Labs/mcp is the official Snowflake server, covering Cortex AI, object management, and SQL orchestration. It scores 61.24 — lower than expected for an official server because the last commit was December 19, 2025, triggering a freshness penalty in the score. It's still the right pick if your stack runs on Snowflake, but watch the repo for resumed activity.

What to watch for

Official vs community servers

Two of the top six are official servers from the vendor or project: dbt Labs and Kubeflow. Official servers track breaking API changes and protocol upgrades — they're safer long-term bets. The others (motherduckdb/mcp-server-motherduck and datalayer/jupyter-mcp-server) are community-maintained but have strong contributor counts and active commit histories, which is the next best signal.

Freshness matters more in data tooling

Data tooling APIs change fast — new Spark versions, GA4 changes, Snowflake protocol updates. A server that hasn't been committed to in 90+ days may be broken against the current API version. That's why freshness is weighted at 25% in the AgentRank score. The Snowflake Labs server is the clearest example: official backing but three months without a commit.

Bus factor risk in solo projects

surendranb/google-analytics-mcp is maintained by a single contributor. For anything production-critical, evaluate whether you could maintain a fork if the author goes inactive. The contributor signal in the AgentRank score (10% weight) specifically flags this risk class.

The missing piece: pandas and Python data analysis

There is no well-maintained, high-scoring MCP server for direct pandas or numpy interaction in the index as of March 2026. The closest is motherduckdb/mcp-server-motherduck for in-memory analytical queries, and datalayer/jupyter-mcp-server for running arbitrary Python. A focused pandas MCP server would fill a real gap in the ecosystem.

Methodology

Tools classified as "data science" via keyword matching on description, topics, and repo name — covering Jupyter, DuckDB, Spark, dbt, Snowflake, analytics, dataframes, notebooks, machine learning pipelines, and related terms. Only non-archived repositories with a computed score included. Data from the AgentRank index crawled March 2026.

Score weights: stars 15%, freshness (days since last commit) 25%, issue health (closed/total ratio) 25%, contributors 10%, inbound dependents 25%.