Best MCP Servers for Data Engineers in 2026

Why data engineers need MCP

Data engineering work is split across tools. You write SQL in one window, check pipeline status in another, browse table schemas in a third, and read documentation in a fourth. Every context switch costs time and mental energy.

MCP servers bring these tools into your AI coding assistant. Instead of switching to pgAdmin to check a table schema, you ask your AI assistant directly. Instead of writing a migration script from memory, the AI reads your current schema and generates it. Instead of manually searching documentation for the right Spark or dbt syntax, the AI pulls it from up-to-date sources.

The StackMCP catalog has 10+ database and data-related MCP servers. Here is how to pick the right ones for data engineering work.

Database MCP servers compared

The foundation of any data engineering stack is direct database access. Here is what is available:

PostgreSQL MCP: The essential server

If you work with PostgreSQL -- and most data engineers do -- PostgreSQL MCP is the first server to install. It is maintained by Anthropic as part of the official MCP server collection, has 79K+ GitHub stars, and gets 40K weekly downloads.

At 8 tools and 4,120 tokens, it is lightweight. You can query tables, inspect schemas, list databases, and run SQL directly from your AI conversation. The AI sees your actual table structures, which means it generates accurate SQL instead of guessing column names.

Practical data engineering uses:

• Explore a new data warehouse schema without leaving your editor
• Generate migration scripts based on the current schema state
• Debug data quality issues by running ad-hoc queries inline
• Profile table sizes and index usage

For a deep comparison with other SQL servers, see PostgreSQL MCP vs SQLite MCP vs MySQL MCP.

Supabase MCP vs Neon MCP: Managed PostgreSQL

If your data sits in managed PostgreSQL, you have two strong options.

Supabase MCP is the heavier choice at 25 tools and 12,875 tokens. Beyond database queries, it manages tables, runs migrations, deploys edge functions, and handles branches. If you use Supabase as your platform, this single server replaces several manual workflows. The tradeoff is token cost -- 12,875 tokens is significant. See How to use Supabase MCP server for a full walkthrough.

Neon MCP gives you 18 tools at 9,270 tokens. Its standout feature for data engineers is branch management. Neon lets you create instant database branches, which is ideal for testing pipeline changes against a copy of production data without affecting the real dataset. Your AI assistant can create a branch, run experimental queries, and delete it when done.

If you just need to query PostgreSQL and do not need platform management features, the base PostgreSQL MCP at 4,120 tokens is the most token-efficient choice.

MongoDB MCP for document stores

Data pipelines that ingest semi-structured data often land it in MongoDB before transforming it into a relational schema. MongoDB MCP gives your AI assistant access to inspect collections, query documents, and analyze document structures. At 6 tools and 3,090 tokens, it is lightweight enough to add alongside a PostgreSQL server.

Redis and Upstash MCP for caching layers

Data pipelines use Redis for caching intermediate results, managing job queues, and storing real-time aggregations. Redis MCP covers basic key-value operations at 4,120 tokens. Upstash MCP adds serverless Redis management at 5,150 tokens, useful if your caching layer runs on Upstash.

Beyond databases: Pipeline and analytics tools

Prisma MCP for schema management

Prisma MCP is not a database server -- it is an ORM layer that manages migrations across PostgreSQL, MySQL, SQLite, and more. For data engineers who use Prisma to manage schema changes, this server lets your AI run migrations, check migration status, and reset databases. At 4,120 tokens and 7.8 million weekly npm downloads, it is battle-tested.

Vector databases for ML pipelines

If your data engineering work feeds into machine learning, vector databases are part of the pipeline:

Pinecone MCP handles vector index management, data upserts, similarity queries, and result reranking. Weaviate MCP adds semantic search and knowledge base operations. Both are official servers from their respective vendors.

These are particularly useful for data engineers building RAG (Retrieval-Augmented Generation) pipelines, where you need to manage embedding indexes alongside traditional data stores.

Monitoring for data pipelines

Pipeline failures need fast diagnosis. Two monitoring servers are relevant:

Sentry MCP tracks errors and performance in your pipeline code. If your ETL jobs are Python or Node.js applications instrumented with Sentry, this gives your AI assistant direct access to error traces.

Grafana MCP is the heavyweight option at 43 tools and 22,145 tokens. If your pipeline metrics flow into Grafana dashboards, this server lets the AI query metrics, check alerts, and review incidents. The token cost is steep, so consider adding it only when actively debugging pipeline performance.

Recommended data engineering stacks

Core stack: SQL-focused data engineering

For data engineers who primarily work with SQL databases and want a lean setup:

This stack is lean at under 19K tokens. SQLite MCP may seem redundant alongside PostgreSQL, but it is valuable for prototyping queries on sample data locally before running them against the warehouse. Context7 MCP adds negligible token overhead but gives your AI access to current documentation for any library in your pipeline.

Full stack: Multi-database data platform

For data engineers managing multiple database systems and pipeline infrastructure:

At roughly 27K tokens, this stack stays within the recommended 15-30K token range while covering relational, document, and cache databases plus schema management. See How to cut MCP token costs in half if you need to trim further.

ML pipeline stack

For data engineers building pipelines that feed machine learning systems:

Setting up your data engineering config

Use the StackMCP config generator to generate the config for your editor. Select the servers you need, add your credentials, and export.

For Claude Code, add database servers with connection strings:

claude mcp add postgres -- npx -y @modelcontextprotocol/server-postgres postgresql://user:pass@host:5432/warehouse
claude mcp add mongodb -e MONGODB_URI=mongodb+srv://user:pass@cluster.mongodb.net/mydb -- npx -y mongodb-mcp
claude mcp add context7 -- npx -y @upstash/context7-mcp

For setup guides for other editors, see Cursor setup or Windsurf setup.

Security note: Database connection strings contain credentials. Use environment variables or a secrets manager rather than hardcoding them in config files. Never commit connection strings to version control. See How to secure your MCP server setup.

What is missing from the ecosystem

The current MCP ecosystem has strong coverage for SQL databases but gaps in dedicated data engineering tools:

• No dbt MCP server -- dbt is the most popular transformation framework, but there is no dedicated MCP server for running dbt commands, inspecting lineage, or managing models. You can work around this by running dbt through the shell, but a dedicated server would be more efficient.
• No Airflow/Dagster MCP server -- Pipeline orchestrators lack MCP integration. Checking DAG runs or triggering backfills still requires the orchestrator's own UI.
• No BigQuery or Snowflake MCP server -- Cloud data warehouses are underserved. You can query them through generic PostgreSQL-compatible interfaces in some cases, but native servers with full feature support are missing.

These gaps are opportunities. The MCP ecosystem is growing fast, and data engineering tools are likely next in line. In the meantime, Context7 MCP can pull documentation for any of these tools, and the Filesystem MCP server lets your AI read and edit pipeline configuration files directly.

Next steps

• Browse the Data Science stack for a pre-built configuration
• PostgreSQL vs SQLite vs MySQL MCP for a detailed database server comparison
• Supabase MCP vs Firebase MCP if you are choosing a managed database platform
• Explore all MCP servers to find servers for your specific data tools