Meet DataBuff: AI-native OpenTelemetry APM
DataBuff (github.com/databufflabs/databuff) is an open-source AI-native OpenTelemetry APM — deploy with one Docker command.
In one line: ingest telemetry via OTel standards first, then let AI read traces, metrics, topology, and alerts — not a chat box bolted onto dashboards.
📊 OpenTelemetry APM foundation
- Native OTLP — traces, service metrics, topology, alerting
- Rich data model derived from traces (services, APIs, flows, component metrics)
- Three components: Ingest → Doris → Web, low ops overhead
🤖 AI-native capabilities
- Multi-agent collaboration: AI brain + metrics / inspection experts
- Smart query: natural language over metrics, traces, topology, alerts
- Health inspection: threshold-free anomaly screening with evidence chains
- **Capability** — What it does - **In-platform AI chat** — Ask in the Web UI: “Why is this trace slow?” or “Inspect order-service” - **MCP for external agents (this article)** — Cursor, Claude Code, OpenClaw call the same APM tools - **Official Skills** — Constrain query semantics and inspection flows — agents don't invent metrics
Screenshot: Ingest → Doris → Platform — one command to run
Screenshot: global topology — AI and MCP tools read the same live data
Screenshot: in-platform AI brain dispatches experts — external agents use the same backend via MCP
This article: DataBuff exposes platform capabilities via MCP + Skills to OpenClaw (and Feishu / DingTalk channels). Below: 5-minute setup for one-sentence whole-cluster inspection.
am alert: five tabs, or one sentence?
Alert fires: “Checkout is slow — anything wrong cluster-wide?”
Old way: APM dashboard → traces → topology → alert list → hand-written summary. 20–30 minutes per round.
Screenshot: multi-tab evidence stitching — agents can do it in one prompt
With OpenClaw: send one message in CLI, WebChat, or Feishu / DingTalk. The agent calls DataBuff MCP against real OTel data in Doris, follows official Skills, and returns an evidence-backed report — not hallucinated numbers.
❌ Without MCP
The model invents “error rate 2.3%” — pure hallucination, not safe for incident channels.
✅ MCP + Skills
inspectService first → traces / topology for anomalies → Chinese or English report with evidence.
Screenshot: OpenClaw activity — Databuff-apm GetCurrentTimeRange / QueryServicesAll
MCP is the hands; Skills are the brain
DataBuff ships an agent integration pack (integrations/agent/). You need both layers for a real ops agent:
MCP = 11 APM tools (services, traces, metrics, alerts, inspection, charts)
Skills = query semantics + inspection workflow (when to inspect, time ranges, evidence chaining)
- **Layer** — Contents · Examples - **MCP tools** — Live data access · queryServicesAll, inspectService, queryTraceDetail - **Skills** — Behavior rules · skill.data.metrics, skill.inspection.health
External agents hit the same Spring beans as in-platform AI experts — not pasted APIs, real OTel data in Doris.
Screenshot: skill.data.metrics enabled on OpenClaw Skills page
-minute setup: MCP + Skills end-to-end
Prerequisite: DataBuff deployed; OpenClaw can reach the Web port.
Step 0 · Get the official integration pack — do not download Skills/MCP from third-party marketplaces:
git clone https://github.com/databufflabs/databuff.git cd databuff/integrations/agent/
Or browse integrations/agent/ on GitHub. Contains skills/ (2 official Skills) and mcp/ (client config examples).
Step 1 · Deploy DataBuff (if not installed):
curl -fsSL https://databuff.ai/databuff/ai-apm-install.sh | bash
Screenshot: one-command install, note Web URL and port
Step 2 · Configure MCP — copy mcp/openclaw-amc-config.example.json, set mcp.url to your DataBuff host:
{ "name": "databuff-apm", "description": "DataBuff APM MCP — metrics, traces, topology, alerts, inspection", "mcp": { "transport": "streamable-http", "url": "http://YOUR\_DATABUFF\_HOST:PORT/mcp" }, "skills": [ "skill.data.metrics", "skill.inspection.health" ] }
Replace YOUR_DATABUFF_HOST:PORT. MVP needs no Authorization header. MCP endpoint: /mcp (Streamable HTTP).
Step 3 · Install Skills — copy integrations/agent/skills/ to your client Skills path:
- **Client** — Skills path - **OpenClaw** — OpenClaw workspace skills/ (Control UI → Skills should list both) - **Cursor** — ~/.cursor/skills/ or project .cursor/skills/ - **Claude Code** — ~/.claude/skills/
# From databuff repo root (OpenClaw example — adjust target path) cp -r integrations/agent/skills/skill.data.metrics /skills/ cp -r integrations/agent/skills/skill.inspection.health /skills/
Each Skill is a directory with SKILL.md. Synced with deploy/common/skills/ — re-copy after updates.
- **skillId** — Purpose - **skill.data.metrics** — APM metrics, trace, alert query semantics (time ranges, which tool) - **skill.inspection.health** — Health inspection flow (inspect first, gather evidence, conclude)
Step 4 · Verify — after restarting OpenClaw Gateway:
- Control UI → Skills:
skill.data.metrics,skill.inspection.health - Control UI → Instances: Gateway online
- New chat → Activity:
Databuff-apmtools (e.g.QueryServicesAll)
Screenshot: Gateway and Control UI connected (127.0.0.1:18789)
Security: MVP has no separate MCP token — use on private network / VPN; add a gateway before public exposure.
Three prompts to validate the full path
①
“List services from the last hour” — Skill parses time → getCurrentTimeRange → queryServicesAll
Screenshot: 7 services in the last hour (service-a / service-b / MySQL, etc.)
②
“Inspect service-a health” — inspectService → queryMetricData / queryServiceAlarms
Screenshot: Activity 3 tools — InspectService / QueryMetricData / QueryServiceAlarms
③
“Chart service-a error rate for the past hour” — queryMetricData + drawTrendCharts
Screenshot: 0% error rate trend — real data, not invented
Pass criteria: tool panel shows queryServicesAll / inspectService with JSON responses — not plain-text guesses.
vs in-platform AI and vs Cursor?
- **Scenario** — Path · Best for - **DataBuff Web UI** — AgentScope JAVA_BEAN + multi-expert · Day-to-day console troubleshooting - **OpenClaw / Feishu / DingTalk** — MCP + Skills · One-liner queries in alert channels - **Cursor / Claude Code** — Same MCP + Skills · Developers querying APM from the IDE
All three paths are independent. OpenClaw brings APM into your existing agent runtime — no extra browser tab for AI incidents.
Full config and FAQ: docs/manual/agent-integration.md and integrations/agent/README.md in the repo.
From guessing metrics to calling real APM
DataBuff opens APM to any agent runtime; OpenClaw / Feishu / DingTalk are just entry points — MCP for real data, Skills for semantics is the core.
⭐ Wire up DataBuff agent integration in 5 minutes
github.com/databufflabs/databuff/integrations/agent
Try: “Inspect service-a, then check if anything is wrong cluster-wide.”
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