This hub collects eleven notes that cover building custom MCP servers for data engineering, from the initial “should I build?” decision through production-ready patterns. They assume familiarity with MCP fundamentals — what the protocol is, how clients and servers communicate, and the three server primitives (tools, resources, prompts).
Reading Order
Getting Started
Custom MCP Server Decision Criteria — When to build custom versus using an existing server. The ecosystem has 5,800+ servers; check before you build.
MCP SDK Selection for Data Engineering — Python (FastMCP) vs. TypeScript (McpServer). For most data engineering teams, the answer is Python, but the note covers when TypeScript makes sense.
FastMCP Server Skeleton — Minimal working servers in both Python and TypeScript. Start here to understand the structure before adding complexity.
Designing and Building
MCP Tool Design Patterns — How to design tools that work well with AI: docstrings as descriptions, Pydantic models for structured output, input validation with schemas.
MCP Resources and Prompts — Beyond tools: resources for read-only data exposure, prompts for reusable templates, and the Context object for progress reporting.
MCP Transport Configuration — Practical setup for stdio (local development) and streamable HTTP (production deployment).
MCP Server Testing and Debugging — The MCP Inspector, the stderr logging gotcha, and a three-stage testing workflow.
MCP Server Project Setup — Step-by-step project initialization: directory structure, dependencies, client installation.
Data Engineering Patterns
Three practical server patterns that address common needs:
MCP Data Catalog Server Pattern — Expose your internal data catalog for AI-assisted discovery: table search, metadata retrieval, lineage tracing.
MCP Pipeline Monitoring Server Pattern — Monitor pipelines, check job status, list failures, trigger reruns across orchestrators.
MCP Data Quality Server Pattern — Run validation checks, retrieve quality scores, surface tables that need attention.
Existing Servers to Study
Before building from scratch, study how established MCP servers handle the same problems:
| Server | Focus | Learn About |
|---|---|---|
| DataHub MCP | Data catalog | Catalog integration, search, lineage APIs |
| dbt MCP | dbt integration | CLI wrapping, hybrid local/remote architecture |
| OpenMetadata MCP | Enterprise metadata | Authentication handling, complex metadata models |
| Elementary MCP | Data observability | Time-series data, anomaly detection, alerts |
These are open source under permissive licenses. Fork them as starting points or adapt their patterns.
Related Fundamentals
- MCP Protocol Architecture — How MCP works at the protocol level
- Security Posture for AI Agents — Security principles for AI tools accessing data infrastructure
- Custom Parameterized MCP Queries — A specific pattern for constrained BigQuery access via MCP
- BigQuery MCP Server Setup — The hub for Google’s official BigQuery MCP options