Pendo 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 Pendo as an MCP tool provider through the Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
ASK AI ABOUT THIS MCP SERVER
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 Pendo. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Pendo?"
)
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 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.
LlamaIndex agents combine Pendo tool responses with indexed documents for comprehensive, grounded answers. Connect 10 tools through the 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
- 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 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 Pendo to LlamaIndex via MCP
Follow these steps to integrate the Pendo 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 Pendo
Why Use LlamaIndex with the Pendo MCP Server
LlamaIndex provides unique advantages when paired with Pendo through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Pendo tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Pendo tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Pendo, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Pendo tools were called, what data was returned, and how it influenced the final answer
Pendo + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Pendo MCP Server delivers measurable value.
Hybrid search: combine Pendo real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Pendo 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 Pendo for fresh data
Analytical workflows: chain Pendo queries with LlamaIndex's data connectors to build multi-source analytical reports
Pendo MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect Pendo to LlamaIndex 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 LlamaIndex
Ready-to-use prompts you can give your LlamaIndex 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 LlamaIndex
Common issues when connecting Pendo to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpPendo + LlamaIndex FAQ
Common questions about integrating Pendo 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 Pendo 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 Pendo to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.