If you're searching for "Amplitude MCP", you're asking one of two things: does Amplitude have an MCP server? or how do I connect Amplitude to an AI assistant via the Model Context Protocol?
✅ Amplitude has an official MCP server. Details in the setup section below.
What Is MCP?
Model Context Protocol (MCP) is an open standard developed by Anthropic that lets AI assistants — like Claude — connect to external tools, APIs, and data sources in a standardised way.
Before MCP, every AI integration required bespoke tooling: custom prompts, custom API wrappers, and custom glue code to pass context back and forth. MCP replaces that with a common interface: the AI asks the MCP server for data or actions, the server returns structured results, and the AI uses them to answer your question or complete a task.
In plain terms: MCP is how you give an AI assistant live access to Amplitude — not just knowledge about it, but real, up-to-date data from your account.
What a Amplitude MCP Integration Does
Once Amplitude is connected via MCP, your AI assistant can:
- Read live data — pull records, metrics, activity, and status directly from Amplitude
- Take actions — create, update, or log records based on your instructions
- Cross-reference context — combine Amplitude data with other connected tools mid-conversation
The key difference from a standard chatbot: the assistant is not working from training data or memory. It is reading your actual Amplitude instance, in real time.
Practical Amplitude MCP Use Cases
Natural-language metric queries
"What was our activation rate last week vs the week before?" The assistant queries Amplitude via MCP and returns the numbers with a plain-English interpretation.
Anomaly explanation
When a metric spikes or drops, the assistant can pull recent data from Amplitude and generate a hypothesis list: campaign changes, product releases, seasonality, tracking issues.
On-demand segment analysis
Ask the assistant to compare behaviour across user segments in Amplitude and return a concise breakdown — without writing a query.
How to Connect Amplitude via MCP
There are two main paths:
Option A: Use Amplitude's official MCP server
Amplitude maintains an official Amplitude MCP server. This is the recommended starting point — it's built and maintained by the Amplitude team, so it stays up to date with API changes.
What the server exposes:
- charts
- dashboards
- experiments
- feature flags
- metrics
- event data
What you'll need:
- An MCP-compatible client (Claude Desktop, OpenClaw, or another host)
- Amplitude credentials configured per the server's setup guide
This path gives you the most control but requires you to handle client configuration and credential management yourself.
Option B: Use Cody (OpenClaw-based, managed)
Cody is built on OpenClaw and supports MCP-compatible integrations out of the box. You connect Amplitude once from the Cody dashboard — no server to run, no code to write — and Cody handles authentication, context passing, and write-back actions with appropriate guardrails.
Cody works where your team already operates: Slack, Telegram, or the web chat. The Amplitude connection is available to your entire team without each person setting up their own MCP client.
Want Amplitude Connected to AI Without Running Your Own MCP Server?
Cody has Amplitude integration built in. Query user behaviour and retention cohorts from Slack without API keys or proxy services.
MCP vs Other AI Integration Patterns
| Approach | What it is | Tradeoff |
|---|---|---|
| MCP | Standardised protocol for live tool access | Requires an MCP server; most powerful when set up correctly |
| RAG (retrieval) | Pre-index Amplitude content and retrieve it | Good for static docs; not suitable for live/transactional data |
| Manual copy-paste | Paste Amplitude output into ChatGPT/Claude | Fast to start; breaks for anything recurring or at scale |
| Custom API wrappers | Bespoke integration code per tool | Full control; high maintenance overhead |
MCP wins when you need live data from Amplitude and want to avoid rebuilding integrations as APIs change.
Common Mistakes
- Using training data when live data is needed — if the AI doesn't have an MCP connection, it will answer from memory, which is often outdated or wrong for account-specific questions
- No write-back guardrails — MCP can write to Amplitude, so it's worth adding an approval step for any action that modifies records
- Too many tools exposed at once — give the AI access to the Amplitude actions it actually needs; a scoped connection is easier to reason about and audit
- Skipping structured outputs — ask the AI to return structured JSON or clear fields when writing back to Amplitude; free-form output is harder to validate
Related MCP Guides
Want the full workflow picture? See: Amplitude AI Automation and How to Connect Amplitude to OpenClaw.