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Version: 1.0

For AI Agents

GreptimeDB is built to work with AI coding agents. An agent can deploy, configure, ingest, and query GreptimeDB on its own through standard protocols (OTLP, Prometheus, MySQL/PostgreSQL, SQL/PromQL), and the resources below help it do that reliably.

Quickest start: one instruction​

The entry-point quickstart guide is hosted as a fetchable markdown file. Give your agent the prompt below and it takes it from there — fetching the guide at runtime, deciding whether GreptimeDB fits, picking the right install and write path, and navigating the docs via llms.txt. No installation required.

GreptimeDB MCP Server​

The GreptimeDB MCP Server implements the Model Context Protocol, so agents can securely explore and analyze your databases — list tables, read data, and run SQL, TQL (PromQL-compatible), and range queries. It enforces read-only access and supports data masking, and ships with built-in scenario templates for metrics, trace, and PromQL analysis, IoT monitoring, table operations, and log pipeline creation.

pip install greptimedb-mcp-server

See the greptimedb-mcp-server repo for transports (stdio, SSE, Streamable HTTP) and configuration.

GreptimeDB Skills​

GreptimeDB already speaks open standards that most coding agents know. For our own features — pipelines, flows, triggers — a Skill teaches the agent the complete workflow and gives it the best results. Skills tell the agent what to do; the MCP Server provides the tools to execute it (for example, the pipeline skill uses dryrun_pipeline to verify a config before applying it).

  • greptimedb-quickstart — the entry point. When to use GreptimeDB, how to install, which write protocol to choose, how to query, plus pointers to deeper docs. Start here.
  • greptimedb-pipeline — parse, transform, and route logs (Learn → Create → Verify → Refine).
  • greptimedb-flow — continuous aggregation and materialized views.
  • greptimedb-trigger — alerting rules, including converting Prometheus alert rules to CREATE TRIGGER (Enterprise).
  • self-monitoring-export — for cluster incidents: infer the log export time range from the user's description, then export cluster self-monitoring logs and metrics for investigation.

Skills follow the Agent Skills open standard, so they work with Claude Code, OpenAI Codex CLI, GitHub Copilot, Cursor, and others. Every Skill is also hosted as a fetchable markdown file at https://docs.greptime.com/skills/<skill-name>/SKILL.md (for example, greptimedb-pipeline), so an agent can load one at runtime without installing anything.

To install a Skill persistently into your agent's config, use the skills CLI:

npx skills add https://github.com/GreptimeTeam/docs/tree/main/skills/greptimedb-quickstart

See the skills repo for the full list and install commands.

Machine-readable docs​

The whole documentation site is built to be consumed by agents, following the llmstxt.org standard:

  • llms.txt — a structured index linking every documentation section with a short description. Point an agent at it to find the right page without crawling the site.
  • llms-full.txt — the entire documentation concatenated into one file, for loading the full corpus at once.
  • Any page as markdown — append .md to any documentation URL to get the raw markdown of that page (for example, /user-guide/integrations/mcp.md).

For ad-hoc questions, the Ask AI assistant on docs.greptime.com answers in plain language, grounded in the official documentation.

References​