PostHog MCP Server for LlamaIndex 10 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add PostHog as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
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Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to PostHog. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in PostHog?"
)
print(response)
asyncio.run(main())
* 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.
LlamaIndex agents combine PostHog tool responses with indexed documents for comprehensive, grounded answers. Connect 10 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
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 LlamaIndex 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 LlamaIndex via MCP
Follow these steps to integrate the PostHog MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
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
Why Use LlamaIndex with the PostHog MCP Server
LlamaIndex provides unique advantages when paired with PostHog through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine PostHog tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain PostHog tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query PostHog, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what PostHog tools were called, what data was returned, and how it influenced the final answer
PostHog + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the PostHog MCP Server delivers measurable value.
Hybrid search: combine PostHog real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query PostHog to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying PostHog for fresh data
Analytical workflows: chain PostHog queries with LlamaIndex's data connectors to build multi-source analytical reports
PostHog MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect PostHog to LlamaIndex 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 LlamaIndex
Ready-to-use prompts you can give your LlamaIndex 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 LlamaIndex
Common issues when connecting PostHog to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpPostHog + LlamaIndex FAQ
Common questions about integrating PostHog MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect PostHog with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect PostHog to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.