Jebbit 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 Jebbit as an MCP tool provider through 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 Jebbit. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Jebbit?"
)
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 Jebbit MCP Server
Empower your AI agents with Jebbit's interactive experience platform. This MCP server allows you to list experiences, retrieve consumer attributes, manage audience segments, and track reporting jobs directly through the Jebbit API. Ideal for leveraging zero-party data and automating marketing insights.
LlamaIndex agents combine Jebbit 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 Jebbit 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 Jebbit to LlamaIndex via MCP
Follow these steps to integrate the Jebbit 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 Jebbit
Why Use LlamaIndex with the Jebbit MCP Server
LlamaIndex provides unique advantages when paired with Jebbit through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Jebbit tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Jebbit tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Jebbit, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Jebbit tools were called, what data was returned, and how it influenced the final answer
Jebbit + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Jebbit MCP Server delivers measurable value.
Hybrid search: combine Jebbit real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Jebbit 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 Jebbit for fresh data
Analytical workflows: chain Jebbit queries with LlamaIndex's data connectors to build multi-source analytical reports
Jebbit MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect Jebbit to LlamaIndex via MCP:
get_account
Use to verify account status. Retrieves account details
get_experience
Use this to understand the structure or overall results of a specific quiz or survey. Retrieves details for a specific experience
list_attributes
g., "favorite_color", "purchase_intent") that have been captured across experiences. Essential for understanding what consumer insights are available. Lists all consumer attributes captured
list_campaigns
Use this to monitor where traffic to experiences is coming from. Lists all active campaigns
list_experiences
Returns experience names, IDs, and publication status. Use this to identify which interactive content is available for analysis. Lists all interactive experiences
list_integrations
Useful for verifying data flow to other platforms. Lists all active integrations
list_reporting_jobs
Use this to check the status of large data requests. Lists all recent reporting jobs
list_segments
Useful for identifying high-value cohorts for targeted marketing. Lists all audience segments
list_users
Useful for account auditing and permission checks. Lists all platform users
list_webhooks
Useful for auditing integrations. Lists all configured webhooks
Example Prompts for Jebbit in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Jebbit immediately.
"List all interactive experiences in my Jebbit account."
"Show me the consumer attributes captured by my quizzes."
"Check for any recent reporting jobs."
Troubleshooting Jebbit MCP Server with LlamaIndex
Common issues when connecting Jebbit to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpJebbit + LlamaIndex FAQ
Common questions about integrating Jebbit 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 Jebbit 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 Jebbit to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.