This document shows you how to use the data lineage remote Model Context Protocol (MCP) server to connect with AI applications including Gemini CLI, ChatGPT, Claude, and custom applications you are developing. The data lineage remote MCP server lets you interact with data lineage to query data lineage graphs, discover upstream data provenance, and analyze downstream impact. The Data Lineage API remote MCP server is enabled when you enable the Data Lineage API API.
Model Context Protocol (MCP) standardizes how large language models (LLMs) and AI applications or agents connect to external data sources. MCP servers let you use their tools, resources, and prompts to take actions and get updated data from their backend service.
What's the difference between local and remote MCP servers?
- Local MCP servers
- Typically run on your local machine and use the standard input and output streams (stdio) for communication between services on the same device.
- Remote MCP servers
- Run on the service's infrastructure and offer an HTTP endpoint to AI applications for communication between the AI MCP client and the MCP server. For more information about MCP architecture, see MCP architecture.
Google and Google Cloud remote MCP servers
Google and Google Cloud remote MCP servers have the following features and benefits:- Simplified, centralized discovery
- Managed global or regional HTTP endpoints
- Fine-grained authorization