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

Built by Vinkius GDPR 10 Tools Framework

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

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

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

asyncio.run(main())
Howuku
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* 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 Howuku MCP Server

Empower your AI agents to analyze user behavior on your websites. This MCP server connects to Howuku (now part of VWO) to list projects, retrieve session recordings, view heatmaps, and monitor user feedback and surveys. Perfect for UX researchers and product managers.

LlamaIndex agents combine Howuku 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.

The Howuku 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 Howuku to LlamaIndex via MCP

Follow these steps to integrate the Howuku MCP Server with LlamaIndex.

01

Install dependencies

Run pip install llama-index-tools-mcp llama-index-llms-openai

02

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

03

Run the agent

Save to agent.py and run: python agent.py

04

Explore tools

The agent discovers 10 tools from Howuku

Why Use LlamaIndex with the Howuku MCP Server

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

01

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

02

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

03

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

04

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

Howuku + LlamaIndex Use Cases

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

01

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

02

Data enrichment: query Howuku to augment indexed data with live information before generating user-facing responses

03

Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Howuku for fresh data

04

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

Howuku MCP Tools for LlamaIndex (10)

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

01

get_me

Gets current authenticated user info

02

get_project

Retrieves details for a specific project

03

list_events

Lists custom tracking events for a project

04

list_feedback

Lists visual feedback items for a project

05

list_funnels

Lists conversion funnels for a project

06

list_heatmaps

Lists heatmaps for a project

07

list_organizations

Lists organizations associated with your account

08

list_projects

Lists Howuku projects (sites)

09

list_recordings

Lists session recordings for a project

10

list_surveys

Lists surveys for a project

Example Prompts for Howuku in LlamaIndex

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

01

"List all my Howuku projects."

02

"Show me recent session recordings for project ID 123."

03

"Check for any new user feedback on my site."

Troubleshooting Howuku MCP Server with LlamaIndex

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

01

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

Howuku + LlamaIndex FAQ

Common questions about integrating Howuku MCP Server with LlamaIndex.

01

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.
02

Can I combine MCP tools with vector stores?

Yes. LlamaIndex agents can query Howuku tools and vector store indexes in the same turn, combining real-time and embedded data for grounded responses.
03

Does LlamaIndex support async MCP calls?

Yes. LlamaIndex's async agent framework supports concurrent MCP tool calls for high-throughput data processing pipelines.

Connect Howuku to LlamaIndex

Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.