Kissmetrics MCP Server for LlamaIndex 7 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Kissmetrics 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 Kissmetrics. "
"You have 7 tools available."
),
)
response = await agent.run(
"What tools are available in Kissmetrics?"
)
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 Kissmetrics MCP Server
Connect your Kissmetrics account to any AI agent to automate your user behavioral analytics and tracking workflows. This MCP server enables your agent to record events, manage user properties, and query complex metric data directly from natural language interfaces.
LlamaIndex agents combine Kissmetrics tool responses with indexed documents for comprehensive, grounded answers. Connect 7 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
- Behavior Tracking — Record custom events for specific users to monitor their journey and interactions
- Profile Management — Set and update user properties and metadata to maintain rich customer profiles
- Identity Resolution — Alias multiple identities to maintain a single, unified view of a user across sessions
- Data Discovery — List all defined event types and property names currently in your account
- Analytical Querying — Retrieve people counts and metric values over time to track business performance
The Kissmetrics MCP Server exposes 7 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 Kissmetrics to LlamaIndex via MCP
Follow these steps to integrate the Kissmetrics 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 7 tools from Kissmetrics
Why Use LlamaIndex with the Kissmetrics MCP Server
LlamaIndex provides unique advantages when paired with Kissmetrics through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Kissmetrics tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Kissmetrics tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Kissmetrics, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Kissmetrics tools were called, what data was returned, and how it influenced the final answer
Kissmetrics + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Kissmetrics MCP Server delivers measurable value.
Hybrid search: combine Kissmetrics real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Kissmetrics 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 Kissmetrics for fresh data
Analytical workflows: chain Kissmetrics queries with LlamaIndex's data connectors to build multi-source analytical reports
Kissmetrics MCP Tools for LlamaIndex (7)
These 7 tools become available when you connect Kissmetrics to LlamaIndex via MCP:
alias_identities
g., linking a browser ID to an email). Link two identities together
list_event_types
List all event types defined in the account
list_property_names
List all property names used in the account
query_metric_data
Requires a metric ID and query parameters. Get data for a specific metric
query_people_count
Get the count of people matching specific criteria
record_event
Requires a person identity (email/ID) and an event name. Record a behavior event for a person
set_person_properties
Set properties for a specific person
Example Prompts for Kissmetrics in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Kissmetrics immediately.
"Record a 'Purchased' event for 'user@example.com' with price 49.99."
"Set the property 'Plan Tier' to 'Enterprise' for 'customer_123'."
"Show me all event types defined in my account."
Troubleshooting Kissmetrics MCP Server with LlamaIndex
Common issues when connecting Kissmetrics to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpKissmetrics + LlamaIndex FAQ
Common questions about integrating Kissmetrics 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 Kissmetrics 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 Kissmetrics to LlamaIndex
Get your token, paste the configuration, and start using 7 tools in under 2 minutes. No API key management needed.