GreptimeDB MCP Server

Overview

The GreptimeDB MCP server gives AI assistants a secure, schema‑aware gateway into GreptimeDB databases. By exposing a small but powerful set of MCP capabilities—listing tables, reading data, and executing SQL queries—it lets conversational agents query real‑time analytics or time‑series data without exposing raw database credentials. This is particularly valuable for developers building AI‑powered dashboards, data exploration tools, or automated reporting systems where the assistant must interact with a live database in a controlled manner.

At its core, the server implements the standard MCP operations: returns the names of all tables in a specified database, while streams tabular data back to the assistant. The more advanced operation lets the agent run arbitrary SQL against GreptimeDB, with results formatted for easy consumption. Prompts and tools are also exposed via , , and , allowing developers to pre‑define common query patterns or conversational templates that the assistant can invoke on demand. This structured approach keeps database interactions predictable and auditable, which is essential in regulated environments or when working with sensitive data.

Key features include:

• Schema‑aware exploration – Agents can discover available tables and columns before crafting queries, reducing guesswork.
• Controlled SQL execution – By routing all commands through the MCP interface, developers can enforce row‑level security, rate limits, or query sanitization.
• Prompt templating – Predefined prompts simplify complex queries, enabling non‑technical users to retrieve insights via natural language.
• Cross‑platform integration – The server is designed to run alongside Claude Desktop, but its generic MCP interface means it can be wired into any AI workflow that supports the protocol.

Real‑world scenarios span from data science teams automating exploratory analysis to customer support bots that pull inventory or sales metrics on demand. For example, a marketing assistant could ask for “the last 30 days of click‑through rates” and receive a concise table, all without manual SQL. In a DevOps context, the assistant could monitor uptime metrics stored in GreptimeDB and trigger alerts when thresholds are breached.

Integrating the GreptimeDB MCP server into an AI workflow is straightforward: configure the server as a named MCP endpoint, then reference its capabilities in prompt templates or tool calls. Because it adheres to the Model Context Protocol, any compliant client—Claude, LlamaIndex, or custom orchestrators—can discover and consume its services automatically. This seamless plug‑in model accelerates development, reduces boilerplate code, and ensures that database access remains consistent across different assistants or use cases.

Overall, the GreptimeDB MCP server turns a powerful time‑series database into an AI‑friendly data source, enabling developers to build intelligent applications that can query, analyze, and act on live data with confidence in security, consistency, and ease of integration.