Choosing which MCP servers to install means navigating thousands of options. The main repository has 75,000+ GitHub stars, every major AI vendor has adopted the protocol, and community directories list over 5,800 servers.

This article maps the current landscape: official servers you can trust, clients worth considering, SDKs for building your own, and the database-specific servers that actually matter for our work.

Official Reference Servers

The MCP Steering Group maintains reference servers that demonstrate best practices. These are the ones you can trust for production use, and they’re good models if you end up building your own.

The actively maintained servers:

The Filesystem server might be one of the most helpful ones, particularly when developing custom agents. AI assistants can read your SQL files, dbt models, and configuration without you copying and pasting everything. Git is similarly practical for repository exploration and code review assistance.

Several original reference servers have been archived and transferred to community or vendor maintenance. They still work, but the core MCP team no longer updates them:

Database Servers:

• PostgreSQL
• SQLite
• Redis

Platform Integrations:

• GitHub (now maintained by GitHub)
• Slack (now maintained by Zencoder)
• Google Drive
• Google Maps
• Brave Search (now maintained by Brave)

Development Tools:

• Puppeteer
• Sentry
• AWS KB Retrieval

The transition to vendor maintenance benefits users. When GitHub maintains the GitHub MCP server, you get better integration and faster updates when their APIs change.

Client Landscape

MCP clients are applications that connect to servers and expose their capabilities to AI models. There are over 300 clients now, with 40+ in active development. Most of us will use just one or two.

The choice depends on how you work. Claude Desktop works well for exploratory analysis, documentation review, and ad-hoc queries. Claude Code fits naturally into terminal-based workflows like running dbt. Cursor or Windsurf make sense when most of your work involves editing files in an IDE, letting you query databases or check pipeline status without context-switching.

Corporate Adopters

The protocol has broad industry support. OpenAI adopting it in March 2025 signaled that MCP extends beyond Anthropic’s ecosystem.

In December 2025, Anthropic donated MCP to the Agentic AI Foundation under the Linux Foundation, making it a vendor-neutral open standard.

SDKs and Repositories

Building your own MCP server requires one of the official SDKs. Most languages are covered.

For data engineers, the Python SDK is the natural choice. Installation:

# Using uv (recommended)
uv add "mcp[cli]"

# Using pip
pip install "mcp[cli]"

Requires Python 3.10+. The SDK includes FastMCP, a high-level framework that handles most of the boilerplate:

from mcp.server.fastmcp import FastMCP

mcp = FastMCP("MyDataServer")

@mcp.tool()
def query_table(table_name: str, limit: int = 100) -> str:
    """Query a table and return results."""
    # Your database logic here
    return f"Results from {table_name}"

if __name__ == "__main__":
    mcp.run(transport="stdio")

The TypeScript SDK works for Node.js environments:

npm install @modelcontextprotocol/server zod

Requires Node.js 18+ (22.7.5+ recommended). Community SDKs also exist for Java, Kotlin, Go, Rust, Swift, Ruby, and PHP.

Data Engineering Servers

Rather than building custom integrations for each AI assistant, these servers provide standardized access to your data infrastructure.

The official Snowflake MCP server from Snowflake Labs includes Cortex AI integration, SQL execution, semantic views, and RBAC support. AI assistants can help with query optimization and data exploration using your actual warehouse data, respecting your existing access controls.

The centralmind/gateway server is worth knowing about if you have multiple databases. A single server can provide access to PostgreSQL, MySQL, ClickHouse, Snowflake, BigQuery, SQL Server, Oracle, SQLite, ElasticSearch, and DuckDB. One configuration file, one server process, access to your entire data landscape.

For streaming infrastructure, mcp-confluent lets AI assistants list topics and schemas, produce and consume messages, and access Confluent Cloud REST APIs. Useful for debugging pipelines or exploring topic contents.

Discovery Resources

The official registry at registry.modelcontextprotocol.io is the authoritative source for verified servers. Community directories aggregate the broader ecosystem:

Getting Started

The Filesystem server is the most universally useful for custom agents. AI assistants can read your SQL files, dbt models, and documentation directly instead of you copying everything into chat windows.

After that, your primary database (Snowflake, BigQuery via MCP Toolbox, PostgreSQL) makes the biggest difference. Schema awareness and sample data access turn generic AI suggestions into ones that actually work with your tables.

Git and the dbt MCP server are worth adding if you do code review or need lineage context, but they’re refinements rather than essentials.