A Model Context Protocol (MCP) server acts as a proxy between an external service that provides context, data, or capabilities to a Large Language Model (LLM) or AI application. MCP servers connect AI applications to external systems such as databases and web services, translating their responses into a format that the AI application can understand.
Server Setup
You must enable MCP servers and set up authentication before use. For more information about using Google and Google Cloud remote MCP servers, see Google Cloud MCP servers overview .
Server Endpoints
An MCP service endpoint is the network address and communication interface (usually a URL) of the MCP server that an AI application (the Host for the MCP client) uses to establish a secure, standardized connection. It is the point of contact for the LLM to request context, call a tool, or access a resource. Google MCP endpoints can be global or regional.
The databaseinsights.googleapis.com MCP server has the following MCP endpoint:
- https://databaseinsights.googleapis.com/mcp
MCP Tools
An MCP tool is a function or executable capability that an MCP server exposes to a LLM or AI application to perform an action in the real world.
The databaseinsights.googleapis.com MCP server has the following tool:
| MCP Tools | |
|---|---|
| get_system_metrics | Fetches system related telemetry data for a given database instance using a PromQL query from Cloud Monitoring. |
Get MCP tool specifications
To get the MCP tool specifications for all tools in an MCP server, use the tools/list
method. The following example demonstrates how to use curl
to list all tools and their specifications currently available within the MCP server.
| Curl Request |
|---|
curl --location 'https://databaseinsights.googleapis.com/mcp' \ --header 'content-type: application/json' \ --header 'accept: application/json, text/event-stream' \ --data '{ "method": "tools/list", "jsonrpc": "2.0", "id": 1 }' |
