Can I record a single event using this agent?

Yes, the `record_event` tool allows you to send a JSON object representing your event directly to any named collection.

How do I calculate the average value of a property?

Use the `average_property` tool by specifying the collection name and the numeric target property you want to average.

Is it possible to see my project's collections?

Absolutely. The `list_collections` tool retrieves a list of all event types (collections) currently stored in your Keen project.

Keen MCP Server for LlamaIndex 10 tools — connect in under 2 minutes

Name: Keen
Availability: InStock
Author: Vinkius

docs.llamaindex.ai/en/stable/module_guides/tools/mcp

Built by Vinkius GDPR 10 Tools Framework

LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Keen as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.

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 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 Keen. "
            "You have 10 tools available."
        ),
    )

    response = await agent.run(
        "What tools are available in Keen?"
    )
    print(response)

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 Keen MCP Server

Connect your Keen.io project to any AI agent to automate data collection and analysis. This MCP server allows your agent to record events and run complex analytical queries (count, sum, average, etc.) directly from natural language.

LlamaIndex agents combine Keen tool responses with indexed documents for comprehensive, grounded answers. Connect 10 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

Event Recording — Send custom event data to any collection in your project instantly
Compute Metrics — Run aggregation queries like count, sum, and average on your event data
Data Discovery — List all event collections, saved queries, and cached datasets
Insight Extraction — Retrieve unique values for specific properties to understand data distribution
Project Oversight — Get comprehensive metadata and configuration details for your Keen project

The Keen 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 Keen to LlamaIndex via MCP

Follow these steps to integrate the Keen 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 Keen

Why Use LlamaIndex with the Keen MCP Server

LlamaIndex provides unique advantages when paired with Keen through the Model Context Protocol.

Data-first architecture: LlamaIndex agents combine Keen tool responses with indexed documents for comprehensive, grounded answers

Query pipeline framework lets you chain Keen tool calls with transformations, filters, and re-rankers in a typed pipeline

Multi-source reasoning: agents can query Keen, a vector store, and a SQL database in a single turn and synthesize results

Observability integrations show exactly what Keen tools were called, what data was returned, and how it influenced the final answer

Keen + LlamaIndex Use Cases

Practical scenarios where LlamaIndex combined with the Keen MCP Server delivers measurable value.

Hybrid search: combine Keen real-time data with embedded document indexes for answers that are both current and comprehensive

Data enrichment: query Keen 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 Keen for fresh data

Analytical workflows: chain Keen queries with LlamaIndex's data connectors to build multi-source analytical reports

Keen MCP Tools for LlamaIndex (10)

These 10 tools become available when you connect Keen to LlamaIndex via MCP:

average_property

Calculate average of a property

count_events

Count total events in a collection

count_unique

Count unique values for a property

get_project_details

Get project configuration and details

list_collections

List all event collections

list_datasets

List cached datasets

list_saved_queries

List all saved queries

record_event

Record a single event to a collection

select_unique

List all unique values for a property

sum_property

Sum numeric values of a property

Example Prompts for Keen in LlamaIndex

Ready-to-use prompts you can give your LlamaIndex agent to start working with Keen immediately.

"Record a 'purchase' event with price 99.99 and user 'user_123' in Keen."

"What is the total count of 'page_view' events?"

"Show me all saved queries in my project."

Troubleshooting Keen MCP Server with LlamaIndex

Common issues when connecting Keen to LlamaIndex through the Vinkius, and how to resolve them.

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

Keen + LlamaIndex FAQ

Common questions about integrating Keen MCP Server with LlamaIndex.

How does LlamaIndex connect to MCP servers?

Use the MCP client adapter to create a connection. LlamaIndex discovers all tools and wraps them as query engine tools compatible with any LlamaIndex agent.