How to Use the Beekeeper MCP in LlamaIndex

Index your Beekeeper conversations directly into LlamaIndex for grounded, searchable knowledge.

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Connect Beekeeper MCP to LlamaIndex

Create your Vinkius account to connect Beekeeper to LlamaIndex and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Beekeeper with LlamaIndex

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Searchable Beekeeper knowledge in LlamaIndex

The `list_messages` tool pulls historical data that LlamaIndex then converts into vector embeddings. This creates a knowledge base grounded in your actual team communication. Your agent queries this index instead of hallucinating answers. It provides accurate, context-aware responses based on the specific messages and streams you've indexed.

RAG pipelines for Beekeeper data

Build RAG applications that combine `get_tenant_info` with internal documents. The server provides the live state while your index maintains the long-term history. This setup allows for complex queries about organizational structure or past decisions. Your agent fetches the latest info and compares it against your existing vector store.

Filter Beekeeper tools in LlamaIndex

Use the allowed_tools filter to restrict which operations your agent can perform. You keep control over your Beekeeper environment while still enabling intelligent search. This keeps your agent focused on specific tasks like information retrieval. You avoid unnecessary API calls by limiting the scope to exactly what the index needs.

Setup guide

Set up Beekeeper MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Beekeeper MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Beekeeper tools.",
)
response = await agent.run("List recent Beekeeper data")

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Common questions about Beekeeper MCP in LlamaIndex

It calls the MCP tools to fetch raw content and then processes that text into your vector store. This makes your message history fully searchable.

Absolutely, it combines live API data with your existing documents. This creates a unified index that your agent queries for grounded answers.

All ingested content is stored in your local vector database. We use ephemeral transport layers to ensure your messages are never cached on our servers.

Install the MCP tool spec and initialize the client with your Vinkius URL. Then, convert the tools to an async list for your FunctionAgent.

Authentication is managed entirely by the Vinkius managed server. You only provide your endpoint token to establish the secure connection.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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