Connect your AI Agents to Pendo in minutes

What is a Pendo MCP?

It's an MCP server that exposes your Pendo product analytics data and functionality to AI agents via tools. Your agents can invoke these tools to query visitor and account records, analyze feature adoption, retrieve guide metrics, search segments, and more.

Pendo offers an official MCP server, but you can also use one from a third-party platform, like Merge Agent Handler.

How can I use the Pendo MCP server?

The use cases naturally depend on the agent you've built, but here are a few common ones:

• Churn risk detection: An agent queries Pendo activity data for accounts that have dropped below a usage threshold in the past 30 days, cross-references those accounts with open renewals in Salesforce, and posts a prioritized list to the customer success Slack channel each Monday morning

• Onboarding completion monitoring: When a new account is created in your CRM, an agent tracks that account's guide completion rate in Pendo and sends a follow-up task to the assigned CSM in Asana once the account finishes the onboarding sequence

• Feature adoption reporting: A product manager asks their AI to pull feature engagement data from Pendo for the past quarter, compare it across user segments, and generate a structured summary for the weekly product review meeting

• Guide targeting updates: When a support ticket volume spikes around a particular feature, an agent identifies the relevant Pendo guide, retrieves its current audience rules, and flags it for the product team to update targeting or content

What are popular tools for Pendo's MCP server?

Here are some of the most commonly used tools:

• query_accounts: retrieves a list of accounts from Pendo filtered by metadata fields or usage criteria. Useful for agents that need to segment accounts by plan type, activity level, or custom properties before taking action

• query_visitors: fetches visitor records based on behavioral or metadata filters. Call this when an agent needs to identify specific users, such as those who have not completed a key workflow or who match a target persona

• query_activity: returns event-level activity data for visitors or accounts over a time range. Good for agents tracking feature engagement trends or detecting drops in product usage

• get_guide_metrics: returns performance data for a specific Pendo guide, including views, completions, and dismissals. Helpful when an agent needs to evaluate whether an in-app guide is driving the intended behavior

• list_segments: returns all defined audience segments in Pendo. Use this when an agent needs to find the right segment before scoping a usage query or updating guide targeting

• get_feature: retrieves metadata and engagement stats for a specific tracked feature. Call this when an agent is generating adoption reports or investigating whether users are discovering a particular capability

What makes Merge Agent Handler's Pendo MCP server better than alternative Pendo MCP servers?

There are several reasons to choose Merge Agent Handler's Pendo MCP server over building your own or using another option:

• Enterprise-grade security and DLP: Merge Agent Handler includes built-in data loss prevention controls that let you block or redact sensitive fields before they reach an agent. For Pendo, this means you can prevent individual visitor identifiers, account-level revenue data, and proprietary usage patterns from being exposed even when the agent has broad read access

• Managed authentication and credentials: Merge stores and refreshes Pendo credentials on your behalf. You never expose raw API keys or OAuth tokens to an agent or manage token rotation manually

• Real-time observability and audit trail: Every tool call made against Pendo is logged with timestamp, tool name, input parameters, and response metadata. You can audit exactly what an agent read or wrote without instrumenting anything yourself

• Tool Packs and controlled access: Tool Packs let you bundle specific Pendo tools with tools from other connectors into a single MCP endpoint, scoped to a specific use case. An agent gets exactly the tools it needs, nothing more

How can I start using Merge Agent Handler's Pendo MCP server?

You can take the following steps:

1. Create or log into your Merge Agent Handler account.

2. Install the Merge CLI by running pipx install merge-api, then run merge configure to link the CLI to your Merge account and merge login to authenticate your session.

3. Register the Agent Handler MCP server with Claude Code by running claude mcp add --transport http agent-handler https://ah-api.merge.dev/mcp, then open Claude Code and run /mcp to confirm agent-handler appears with a connected status.

4. Select agent-handler from the MCP list. This opens a browser window where you select which integrations to authenticate. Choose Pendo and complete the auth flow. Merge stores and manages the credentials going forward.

5. Open a Claude Code session and start querying Pendo data directly. The first time you use a Pendo tool, a Magic Link may appear to complete connector authentication.

If you want to connect Merge Agent Handler's Pendo MCP with internal or customer-facing agentic products, you can follow the steps in our docs.

Can employees use Merge Agent Handler to connect their AI tools to Pendo?

Yes, Agent Handler for Employees lets your employees connect Claude, ChatGPT, Microsoft Copilot, Cursor, and other MCP-compatible AI tools to Pendo without bypassing IT governance.

Instead of setting up direct connections with personal credentials that IT can't monitor or revoke, each employee authenticates through Agent Handler and gets individual credentials tied to their identity. A product manager, for instance, can ask their AI to pull feature adoption data, query a visitor segment, or retrieve guide performance metrics, all within the permissions IT has defined.

IT also provisions access by role or group via SCIM. A customer success manager, for example, gets Pendo access to monitor account usage trends, Salesforce to track renewal health, and Slack to flag at-risk accounts with the team; while a product analyst gets Pendo access to query visitor activity and segment data, Looker to build adoption dashboards, and Jira to link usage findings to roadmap tickets.

Every tool call an employee's AI makes to Pendo is also inspected against your DLP rules and logged to a searchable audit trail, giving security teams full visibility into what data was accessed and by whom.