How do I create a PostHog Personal API Key?

Log in to [**PostHog**](https://app.posthog.com), click your profile icon > **Personal API keys**, then **+ Create a Personal API Key**. Give it a label, select the required scopes and copy the key immediately — it won't be shown again after page refresh.

Can I toggle feature flags via the agent?

Yes! Use `create_feature_flag` to add new flags, `update_feature_flag` to modify existing ones (name, description, enabled status), and `delete_feature_flag` to remove them. You'll need the numeric flag ID from `list_feature_flags` for update and delete operations.

What's the difference between events and persons?

**Events** are actions that happen in your product (pageviews, button clicks, purchases). **Persons** are the users who perform those actions, identified by distinct_id. Events are linked to persons, so you can see what actions a specific user has taken. Use `list_events` to browse activity and `get_person` to see a user's profile.

Can I use this with a self-hosted PostHog instance?

Yes! The tools default to `https://app.posthog.com` for the US cloud, but you can set the `host` parameter to your self-hosted instance URL (e.g. `https://posthog.yourcompany.com`). EU cloud users should set the host to `https://eu.posthog.com`.

PostHog Alternative MCP Server for Pydantic AI 13 tools — connect in under 2 minutes

Name: PostHog Alternative
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 13 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect PostHog Alternative through the 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 Alternative "
            "(13 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in PostHog Alternative?"
    )
    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 Alternative MCP Server

Connect your PostHog account to any AI agent and gain full control over your product analytics, feature flags and user cohorts through natural conversation.

Pydantic AI validates every PostHog Alternative tool response against typed schemas, catching data inconsistencies at build time. Connect 13 tools through the 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

User & Project Discovery — Verify your account access and list all analytics projects
Feature Flag Management — List, create, update and delete feature flags with rollout configuration
Cohort Inspection — Review all behavioral cohorts and their filter definitions
Person Analytics — Look up individual users by distinct ID, view their properties and activity timeline
Event Tracking — Browse recent events, filter by event type and inspect event properties
Timeline Annotations — Create and review annotations that correlate metric changes with deployments or launches

The PostHog Alternative MCP Server exposes 13 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 Alternative to Pydantic AI via MCP

Follow these steps to integrate the PostHog Alternative 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 13 tools from PostHog Alternative with type-safe schemas

Why Use Pydantic AI with the PostHog Alternative MCP Server

Pydantic AI provides unique advantages when paired with PostHog Alternative 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 Alternative 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 Alternative connection logic from agent behavior for testable, maintainable code

PostHog Alternative + Pydantic AI Use Cases

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

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

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

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

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

PostHog Alternative MCP Tools for Pydantic AI (13)

These 13 tools become available when you connect PostHog Alternative to Pydantic AI via MCP:

create_annotation

Annotations appear on insights graphs and help correlate metric changes with deployments, launches or incidents. Requires the content text. Optionally set a date_marker (ISO 8601 date). Create a new annotation in PostHog

create_feature_flag

Requires the flag key (unique identifier). Optionally set the name, description, enabled status, rollout percentage and filters. The key must be unique across all flags in the project. Create a new PostHog feature flag

delete_feature_flag

All targeting conditions, release conditions and experiment data associated with the flag will be deleted. Provide the numeric flag ID. WARNING: this action is irreversible. Delete a PostHog feature flag

get_feature_flag

Provide the numeric flag ID from list_feature_flags. Get details for a specific PostHog feature flag

get_person

Provide the distinct_id used to identify the person. Get details for a specific person in PostHog

get_user

Returns user ID, email, name, organization membership and permissions. Use this to verify your API key is working and check your access level. Get the current PostHog user details

list_annotations

Annotations are markers on timeline graphs that highlight important events like deployments, feature launches or incidents. Returns annotation ID, content, date marker and whether it is pinned. List annotations in PostHog

list_cohorts

Each cohort is a dynamic group of users defined by event-based or property-based filters. Returns cohort ID, name, description, whether it is calculated or static. List behavioral cohorts in PostHog

list_events

Optionally filter by event name (e.g. "pageview", "signup", "purchase") and set a limit. Each event includes the event name, timestamp, person distinct ID and properties. List events tracked in PostHog

list_feature_flags

Each flag has a key, name, enabled status, rollout percentage, filters and release conditions. Returns flag ID, key, name, whether it is active, and the targeting configuration. Use this to audit feature flag coverage. List all feature flags in PostHog

list_persons

Each person has distinct IDs, properties, creation date and last event timestamp. Optionally set limit (default 20) and offset for pagination. List persons (users) tracked in PostHog

list_projects

Each project is an analytics workspace with its own events, persons, feature flags and cohorts. Returns project ID, name, organization and creation date. List PostHog projects

update_feature_flag

Provide the flag ID and any fields to change: name, description, enabled status. Only the fields you provide will be updated. Update an existing PostHog feature flag

Example Prompts for PostHog Alternative in Pydantic AI

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

"Show me all feature flags and which ones are enabled."

"Create an annotation for today's deployment of version 3.2.0."

"Show me the profile of user 'user_12345'."

Troubleshooting PostHog Alternative MCP Server with Pydantic AI

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

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

PostHog Alternative + Pydantic AI FAQ

Common questions about integrating PostHog Alternative 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 Alternative MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.