Overview

The OpenGemini MCP Server bridges the gap between AI assistants and time‑series data stored in CNCF OpenGemini. By exposing a set of well‑defined MCP capabilities, it lets assistants like Claude query and analyze databases without needing direct database credentials or custom drivers. This eliminates the friction that developers face when integrating AI with monitoring, telemetry, or IoT data pipelines.

At its core, the server offers four key tools:

• returns every database available on the OpenGemini cluster, giving assistants a quick inventory of data sources.
• lists all measurements within a chosen database, enabling assistants to discover the specific tables or series that hold the data of interest.
• fetches a sample of rows from a measurement, allowing the assistant to preview schema and content before running heavier queries.
• runs arbitrary InfluxQL statements that begin with or , giving developers the flexibility to retrieve complex aggregates, time‑range slices, or metadata while still keeping operations safe and controlled.

These capabilities are intentionally limited to read‑only operations, which enhances security by preventing accidental writes or destructive queries. For developers building analytical workflows, the server’s simplicity means they can quickly add a data‑access layer to an AI assistant without writing new connectors or handling authentication logic. The server can be launched as a Python module, and its configuration is straightforward—environment variables point to the OpenGemini host, port, user, and password, while Claude Desktop picks up the MCP server definition from its configuration file.

Typical use cases include:

• Real‑time monitoring dashboards where an assistant can pull the latest metrics and surface anomalies.
• Root‑cause analysis by querying historical telemetry to correlate events with system changes.
• Data exploration and onboarding for new team members, who can ask an assistant to list available datasets or show sample data.
• Automated reporting that pulls summarized statistics from OpenGemini and formats them into natural‑language summaries.

Because the server adheres to MCP’s standard interface, it can be swapped out or extended with minimal impact on existing AI workflows. Developers benefit from a secure, declarative way to expose structured data to assistants, enabling richer interactions and faster time‑to‑value for analytics projects that rely on OpenGemini.