posthog · MCP Server

Connect PostHog to AI with Improvado MCP

Improvado's MCP server connects PostHog to Claude, Cursor, and other AI agents. Query your PostHog data in natural language — no manual exports or API scripts required.

46K+ metrics ·Read & Write access ·500+ platforms ·<60s setup

Read

Read: Instant Answers from PostHog

Stop building insight queries and funnel reports manually. Ask your AI agent to surface user behavior trends, conversion drop-offs, feature adoption rates, and session replay insights — across any event, cohort, or time range.

Example prompts

"What's the conversion rate for our signup funnel over the last 30 days, and at which step do we lose the most users?"

20 min → 30 sec

"Which feature flags are currently active and what percentage of users have each flag enabled?"

15 min → 20 sec

"Show me the top 10 events by volume in the last 7 days, broken down by platform (web vs mobile) and user segment."

25 min → 45 sec

Works with Claude ChatGPT Cursor +5

Write

Write: Automate PostHog Configuration

Manage feature flags, create annotations, and update cohorts without touching the PostHog UI. Your AI agent handles configuration changes from a single prompt — tested and documented.

Example prompts

"Enable the 'new_checkout_flow' feature flag for 10% of users in the US, targeting users who signed up in the last 90 days."

15 min → 1 min

"Create an annotation on today's date marking the production deployment of v2.4.0 for reference in trend charts."

5 min → 20 sec

"Create a new cohort of users who triggered 'purchase_completed' at least twice in the last 30 days but have not triggered 'subscription_started'."

20 min → 1 min

Every action logged · Fully reversible · SOC 2 certified

Monitor

Monitor: Catch Product Regressions Before Users Do

Set AI-powered watches on key product metrics, event volumes, and conversion rates. Get context-aware alerts when funnels degrade, feature adoption drops, or critical events spike unexpectedly.

Example prompts

"Alert me if the checkout funnel conversion rate drops more than 10% compared to the 7-day rolling average."

Manual → auto

"Every Tuesday: send a product health digest — weekly active users, top 5 events, funnel conversion rates, and any cohorts showing unusual churn."

2 hrs → auto

"Flag if any event that was in the top 20 by volume last week drops out of the top 50 this week — may indicate a tracking regression."

Manual → auto

Alerts sent to Slack, email, or your AI agent

Full cycle

The Closed Loop: Read → Decide → Write → Monitor

Your AI agent doesn't just surface data — it acts. Adjust pricing, update product descriptions, manage inventory, apply discounts — all through natural language. The MCP server translates intent into API operations.

Every phase runs through the same MCP connection. One protocol, all platforms, full governance. No switching between tools.

Ideate

Launch

Measure

Analyze

Report

Iterate

One conversation. All six phases. Every platform.

The daily grind

Common problems. Direct answers.

Challenge 1

Funnel Analysis Requires Too Many Manual Steps

The problem

Building a funnel in PostHog means selecting events, setting the time window, choosing cohorts, waiting for the query to run, and then exporting results to share with the team. Iterating on funnel definitions — changing a step, adjusting the window — resets the whole process.

How MCP solves it

Ask your AI agent to analyze any funnel in natural language. Describe the steps you care about, and the agent queries PostHog, returns conversion rates per step, and surfaces the highest drop-off point — in one interaction.

Try asking

Analyze the activation funnel: signed_up → email_verified → onboarding_complete → first_action. What's the conversion rate at each step for users who signed up in the last 14 days?

Answer in seconds

All data sources, one query

Challenge 2

Feature Flag Impact Is Invisible Without Manual Comparison

The problem

After rolling out a feature flag, teams need to compare behavior between control and treatment groups — conversion rates, engagement, retention. Setting up that comparison in PostHog requires creating separate cohorts, building parallel insight queries, and manually comparing results. It takes hours per experiment.

How MCP solves it

Ask your AI agent to run a feature flag impact analysis in one prompt. It segments users by flag exposure, computes the relevant metrics for both groups, and returns a statistically meaningful comparison.

Try asking

Compare the 7-day retention rate between users who have the 'new_dashboard_layout' feature flag enabled vs disabled. Use signups from the last 30 days as the cohort.

Full detail preserved

No data loss on export

Challenge 3

Connecting Product Events to Business Outcomes Requires SQL

The problem

Answering 'which product behaviors predict conversion to paid?' requires joining PostHog event data with CRM or billing data — a task that demands data engineering time, SQL expertise, and pipeline maintenance. Most product teams can't self-serve this question.

How MCP solves it

Improvado MCP connects PostHog alongside your CRM and billing data. Ask a single question that spans product events and business outcomes. The agent handles the join and returns the answer without any SQL or pipeline work.

Try asking

Which PostHog events in the first 7 days after signup most strongly predict a user converting to a paid plan? Use the last 90 days of data.

Unified data model

Compare anything side by side

👥 Teams

One Framework. Five Roles. Zero Setup.

Same MCP connection, different workflows for every team member. Each role asks in natural language — the MCP server handles the complexity (rate limits, auth, schema normalization, governance) behind the scenes.

Agency CEO

Portfolio health. Client risk. Revenue signals.

Media Strategist

70% strategy, not 70% ops. Auto campaign QA.

Marketing Analyst

Zero wrangling. Cross-platform. AI narratives.

Account Manager

QBR decks auto-generated. Call prep in 30s.

Creative Director

Performance-to-brief. Predict winners before spend.

FAQ

Common questions

What is PostHog MCP?

PostHog MCP is a Model Context Protocol server that connects your PostHog product analytics data to AI agents like Claude, ChatGPT, and Gemini. It lets you query events, funnels, cohorts, feature flags, and session data in natural language — without building PostHog insights manually — all through Improvado's hosted MCP server.

Which PostHog data can I access through the MCP server?

Events, persons, sessions, funnels, cohorts, feature flags, experiments, annotations, insights, and dashboards. Both raw event data and computed metrics are accessible through the AI agent.

Can the AI agent modify PostHog settings or only read analytics?

Both. Read operations cover querying events, funnels, cohorts, and feature flag states. Write operations include enabling/disabling feature flags, updating rollout percentages, creating cohorts, and adding annotations. Permission scope is controlled by your PostHog API key.

Does this work across multiple PostHog projects?

Yes. You can configure access to multiple PostHog projects and query them in a single conversation. This is particularly useful for teams running separate projects per environment (staging vs production) or per product line.

Is my PostHog data secure through the MCP server?

Yes. Improvado stores all PostHog API keys in an encrypted vault certified to SOC 2 Type II. The AI model never has direct access to credentials — requests flow through Improvado's secure proxy with prompt injection protection built in.

How quickly can I set this up?

Under 60 seconds. Add the MCP server URL to your Claude Desktop or Cursor config, provide your PostHog project API key and host, and start querying. No additional infrastructure required — all through Improvado's hosted MCP server.

Stop Reporting. Start Executing.

Connect your data to an AI agent in under 60 seconds. The closed loop starts with one conversation.

SOC 2 Type II GDPR 500+ Platforms