Where do I find my Project ID?

You can find your Project ID in PostHog under **Project Settings**. It is a numeric ID usually found at the top of the settings page.

Does this support PostHog EU instances?

Yes! Use the optional `POSTHOG_HOST` credential and set it to `https://eu.posthog.com`. By default, it uses the US host (`https://app.posthog.com`).

Can I see individual user properties?

Absolutely. Use the `get_person` tool with a Person ID to retrieve all properties, tags, and identification metadata for a specific user.

PostHog MCP Server for Pydantic AI 10 tools — connect in under 2 minutes

Name: PostHog
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 10 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect PostHog through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to PostHog "
            "(10 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in PostHog?"
    )
    print(result.data)

asyncio.run(main())

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Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About PostHog MCP Server

Connect your PostHog project to any AI agent and take full control of your product analytics and feature management through natural conversation.

Pydantic AI validates every PostHog tool response against typed schemas, catching data inconsistencies at build time. Connect 10 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.

What you can do

Insight Exploration — List and retrieve detailed metadata for saved insights, including trends, funnels, and retention charts.
User Tracking — List identified persons and inspect their properties to understand individual user behavior.
Feature Management — Maintain a clear view of all feature flags and their current configurations.
Experiment Monitoring — List active and past experiments to track product improvements and results.
Event Auditing — List the most recent events captured by your project to verify data ingestion and user actions.

The PostHog MCP Server exposes 10 tools through the Vinkius. Connect it to Pydantic AI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect PostHog to Pydantic AI via MCP

Follow these steps to integrate the PostHog MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 10 tools from PostHog with type-safe schemas

Why Use Pydantic AI with the PostHog MCP Server

Pydantic AI provides unique advantages when paired with PostHog through the Model Context Protocol.

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your PostHog integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your PostHog connection logic from agent behavior for testable, maintainable code

PostHog + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the PostHog MCP Server delivers measurable value.

Type-safe data pipelines: query PostHog with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple PostHog tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query PostHog and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock PostHog responses and write comprehensive agent tests

PostHog MCP Tools for Pydantic AI (10)

These 10 tools become available when you connect PostHog to Pydantic AI via MCP:

get_event

Get details for a specific event

get_insight

Get details for a specific insight

get_person

Get details for a specific person

list_actions

List defined user actions

list_dashboards

List project dashboards

list_events

List recent project events

list_experiments

List all active and past experiments

list_feature_flags

List all feature flags

list_insights

) for the project. List PostHog insights

list_persons

List identified persons/users

Example Prompts for PostHog in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with PostHog immediately.

"List all saved insights in our PostHog project."

"Check the status of all feature flags."

"List the last 5 persons identified in our project."

Troubleshooting PostHog MCP Server with Pydantic AI

Common issues when connecting PostHog to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

PostHog + Pydantic AI FAQ

Common questions about integrating PostHog MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer. your PostHog MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.