Choose MCP Server

Overview

The Choose MCP Server is a lightweight, cloud‑native Model Context Protocol (MCP) server designed to bridge Claude Desktop with structured data sources and analytical pipelines. It solves the common developer pain point of having to manually configure multiple data connectors, manage authentication, and expose dataset metadata in a format that Claude can consume directly. By packaging these responsibilities into a single MCP server, developers can focus on building conversational AI experiences instead of plumbing infrastructure.

At its core, the server exposes a set of MCP resources that represent Google Cloud datasets and optional dbt artifacts. When a user queries Claude, the assistant can request contextual information—such as table schemas, row counts, or lineage graphs—from the server. The server then translates these requests into efficient Google Cloud BigQuery queries or dbt manifest lookups, returning structured JSON that Claude can parse and incorporate into responses. This tight integration enables richer, data‑driven conversations without exposing raw query logic to the user.

Key capabilities include:

• Dynamic dataset discovery: A single configuration string can list multiple BigQuery datasets; the server automatically registers them as MCP resources.
• Optional dbt integration: When a dbt manifest file is provided, the server exposes model lineage and metadata, allowing Claude to reference transformation logic or source tables.
• Secure authentication: By leveraging Google Cloud’s Application Default Credentials, the server inherits the same IAM permissions as the developer’s environment, ensuring that data access follows existing security policies.
• Tool invocation: The server can be called as an MCP tool, enabling Claude to perform on‑demand queries or transformations as part of a broader workflow.

Typical use cases include:

• Data‑aware analytics assistants: A business analyst asks Claude to summarize sales trends; the assistant pulls live data from BigQuery and returns a concise report.
• ETL debugging: A data engineer queries the server for lineage of a problematic table, receiving dbt model dependencies and build status.
• Business intelligence: Non‑technical stakeholders interact with Claude to retrieve KPIs, while the underlying MCP server handles query optimization and caching.

Integration is straightforward: once the MCP server is registered in Claude Desktop’s configuration, any model that supports MCP can reference it by name. The server’s simple environment‑variable based configuration makes it easy to deploy in CI/CD pipelines or on local machines, and its optional dbt support gives teams a single point of truth for both raw data and transformation logic. This combination of simplicity, security, and rich metadata exposure makes the Choose MCP Server a valuable addition to any developer’s AI toolkit.