How do I find my Pendo Integration Key?

In Pendo, navigate to **Settings** > **Integrations**. There you can find or generate your Integration Key for API access.

Can I see real-time statistics for my guides?

Yes! Use the `get_pendo_guide_metrics` tool with a Guide ID to retrieve live data on views, clicks, and completion rates.

Does this support looking up individual user metadata?

Absolutely. Use the `get_pendo_visitor` tool to retrieve all identified properties, account association, and activity history for a specific user ID.

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

Name: Pendo
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 Pendo 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 Pendo "
            "(10 tools)."
        ),
    )

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

Connect your Pendo subscription to any AI agent and take full control of your product adoption and user engagement workflows through natural conversation.

Pydantic AI validates every Pendo tool response against typed schemas, catching data inconsistencies at build time. Connect 10 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

Guide Management — List all in-app guides and retrieve detailed metadata and performance metrics.
User & Account Insights — Look up detailed profiles for visitors and accounts to understand their journey.
Product Tagging Auditing — List defined pages and features to verify your product instrumentation.
Metadata Schema Discovery — Retrieve schemas for visitor and account metadata to understand available data points.
Segment Overview — List saved user segments to maintain visibility over your audience targeting.

The Pendo 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 Pendo to Pydantic AI via MCP

Follow these steps to integrate the Pendo 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 Pendo with type-safe schemas

Why Use Pydantic AI with the Pendo MCP Server

Pydantic AI provides unique advantages when paired with Pendo 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 Pendo integration code

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

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

Pendo + Pydantic AI Use Cases

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

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

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

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

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

Pendo MCP Tools for Pydantic AI (10)

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

get_pendo_account

Get details for a specific account

get_pendo_guide

Get details for a specific guide

get_pendo_guide_metrics

Get performance metrics for a guide

get_pendo_visitor

Get details for a specific visitor

list_pendo_applications

List applications tracked in the Pendo subscription

list_pendo_features

List tagged features

list_pendo_guides

) defined in Pendo. List Pendo guides

list_pendo_metadata_schema

List metadata schema definitions

list_pendo_pages

List tagged pages

list_pendo_segments

List saved user segments

Example Prompts for Pendo in Pydantic AI

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

"List all active guides in my Pendo account."

"Get metadata for visitor 'user@example.com'."

"Show me the performance metrics for the guide 'guide_98765'."

Troubleshooting Pendo MCP Server with Pydantic AI

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

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Pendo + Pydantic AI FAQ

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