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 CrewAI 10 tools — connect in under 2 minutes

Name: Pendo
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 10 Tools Framework

Connect your CrewAI agents to Pendo through the Vinkius — pass the Edge URL in the `mcps` parameter and every Pendo tool is auto-discovered at runtime. No credentials to manage, no infrastructure to maintain.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

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Vinkius supports streamable HTTP and SSE.

python

from crewai import Agent, Task, Crew

agent = Agent(
    role="Pendo Specialist",
    goal="Help users interact with Pendo effectively",
    backstory=(
        "You are an expert at leveraging Pendo tools "
        "for automation and data analysis."
    ),
    # Your Vinkius token — get it at cloud.vinkius.com
    mcps=["https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"],
)

task = Task(
    description=(
        "Explore all available tools in Pendo "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 10 available tools "
        "and what they can do."
    ),
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

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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.

When paired with CrewAI, Pendo becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call Pendo tools autonomously — one agent queries data, another analyzes results, a third compiles reports — all orchestrated through the Vinkius with zero configuration overhead.

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 CrewAI 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 CrewAI via MCP

Follow these steps to integrate the Pendo MCP Server with CrewAI.

Install CrewAI

Run pip install crewai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com

Customize the agent

Adjust the role, goal, and backstory to fit your use case

Run the crew

Run python crew.py — CrewAI auto-discovers 10 tools from Pendo

Why Use CrewAI with the Pendo MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with Pendo through the Model Context Protocol.

Multi-agent collaboration lets you decompose complex workflows into specialized roles — one agent researches, another analyzes, a third generates reports — each with access to MCP tools

CrewAI's native MCP integration requires zero adapter code: pass the Vinkius Edge URL directly in the `mcps` parameter and agents auto-discover every available tool at runtime

Built-in task delegation and shared memory mean agents can pass context between steps without manual state management, enabling multi-hop reasoning across tool calls

Sequential and hierarchical crew patterns map naturally to real-world workflows: enumerate subdomains → analyze DNS history → check WHOIS records → compile findings into actionable reports

Pendo + CrewAI Use Cases

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

Automated multi-step research: a reconnaissance agent queries Pendo for raw data, then a second analyst agent cross-references findings and flags anomalies — all without human handoff

Scheduled intelligence reports: set up a crew that periodically queries Pendo, analyzes trends over time, and generates executive briefings in markdown or PDF format

Multi-source enrichment pipelines: chain Pendo tools with other MCP servers in the same crew, letting agents correlate data across multiple providers in a single workflow

Compliance and audit automation: a compliance agent queries Pendo against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

Pendo MCP Tools for CrewAI (10)

These 10 tools become available when you connect Pendo to CrewAI 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 CrewAI

Ready-to-use prompts you can give your CrewAI 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 CrewAI

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

MCP tools not discovered

Ensure the Edge URL is correct. CrewAI connects lazily when the crew starts — check console output.

Agent not using tools

Make the task description specific. Instead of "do something", say "Use the available tools to list contacts".

Timeout errors

CrewAI has a 10s connection timeout by default. Ensure your network can reach the Edge URL.

Rate limiting or 429 errors

The Vinkius enforces per-token rate limits. Check your subscription tier and request quota in the dashboard. Upgrade if you need higher throughput.

Pendo + CrewAI FAQ

Common questions about integrating Pendo MCP Server with CrewAI.

How does CrewAI discover and connect to MCP tools?

CrewAI connects to MCP servers lazily — when the crew starts, each agent resolves its MCP URLs and fetches the tool catalog via the standard tools/list method. This means tools are always fresh and reflect the server's current capabilities. No tool schemas need to be hardcoded.

Can different agents in the same crew use different MCP servers?

Yes. Each agent has its own mcps list, so you can assign specific servers to specific roles. For example, a reconnaissance agent might use a domain intelligence server while an analysis agent uses a vulnerability database server.

What happens when an MCP tool call fails during a crew run?

CrewAI wraps tool failures as context for the agent. The LLM receives the error message and can decide to retry with different parameters, fall back to a different tool, or mark the task as partially complete. This resilience is critical for production workflows.

Can CrewAI agents call multiple MCP tools in parallel?

CrewAI agents execute tool calls sequentially within a single reasoning step. However, you can run multiple agents in parallel using process=Process.parallel, each calling different MCP tools concurrently. This is ideal for workflows where separate data sources need to be queried simultaneously.

Can I run CrewAI crews on a schedule (cron)?

Yes. CrewAI crews are standard Python scripts, so you can invoke them via cron, Airflow, Celery, or any task scheduler. The crew.kickoff() method runs synchronously by default, making it straightforward to integrate into existing pipelines.