PostHog MCP Server

Overview

The PostHog MCP server is a lightweight, open‑source implementation of the Model Context Protocol that allows AI assistants to tap into PostHog’s rich telemetry and analytics ecosystem. By exposing a standardized set of resources, tools, prompts, and sampling endpoints, the server enables developers to enrich their AI workflows with real‑time product data without writing custom connectors.

For teams that rely on PostHog for event tracking, user segmentation, and feature flagging, the MCP server solves a common pain point: integrating that data into conversational agents or code assistants. Instead of building bespoke APIs, developers can simply register the MCP server and then ask Claude or other supported assistants to query event streams, retrieve user cohorts, or generate insights directly from the model context. This eliminates friction between product analytics and AI tooling, allowing analysts, developers, and product managers to ask questions like “What was the conversion rate for users who installed feature X last week?” and receive instant, context‑aware answers.

Key capabilities of the PostHog MCP server include:

• Resource discovery – The server lists all available data streams (events, sessions, properties) in a machine‑readable format so the assistant can reference them dynamically.
• Tool execution – Built‑in tools let the model run SQL‑like queries against PostHog’s internal database, fetch aggregated metrics, or trigger feature flag evaluations.
• Prompt templates – Pre‑defined prompts guide the assistant to ask for necessary parameters (date ranges, cohort definitions) before executing a query.
• Sampling control – The server can limit the amount of data returned, ensuring that large datasets are summarized or paginated to keep responses concise and fast.

Real‑world scenarios include: a product analyst using Claude in VS Code to generate monthly churn reports on the fly; a developer leveraging Cursor’s AI editor to debug feature flag logic by querying live cohort data; or an ops engineer running a Zed‑based assistant to monitor system health metrics and receive alerts when thresholds are breached. In each case, the MCP server acts as a bridge that translates natural language into structured analytics queries and returns actionable results directly within the AI interface.

What sets PostHog’s MCP server apart is its tight integration with an already mature analytics platform. Because it runs within the PostHog monorepo, updates to data schemas or new feature flags automatically propagate to the MCP endpoints. This means developers can rely on a single source of truth for both product data and AI tooling, reducing maintenance overhead and accelerating time to insight.