How to Use the Bonusly MCP in LlamaIndex

Index your team culture with LlamaIndex. Ground your RAG applications in real-time Bonusly recognition data.

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Connect Bonusly MCP to LlamaIndex

Create your Vinkius account to connect Bonusly 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 Bonusly with LlamaIndex

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Index recognition data in LlamaIndex

Turn the output of `list_bonuses` into searchable knowledge. Your index stores this history so the agent can answer questions about past team achievements. This turns raw API data into a context-aware memory bank. You no longer have to guess who did what; the agent searches the index for specific recognition events.

Ground agent answers with Bonusly

Connect `get_leaderboard` to your RAG pipeline. When a user asks about top performers, the agent retrieves the current list instead of hallucinating. This ensures your answers are based on live stats. The agent retrieves the latest data from the server, keeping your knowledge base current.

Query employee records via LlamaIndex

Use `get_user` to pull profile details into your index. This allows the agent to reference specific employee contributions when drafting internal reports. It makes your document generation much more precise. You combine live Bonusly profiles with your existing knowledge base to build comprehensive team summaries.

Setup guide

Set up Bonusly 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 Bonusly 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 Bonusly tools.",
)
response = await agent.run("List recent Bonusly data")

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Common questions about Bonusly MCP in LlamaIndex

You wrap the server tools into a spec and pass them to your agent. This allows the agent to treat API results as data points for its knowledge index.

Absolutely. Once the data is indexed, the agent performs semantic search across your recognition history to find specific patterns or rewards.

We store your sensitive employee recognition data in an ephemeral sandbox. Access is restricted to the specific tools you authorize for your agent.

You can automate it. Set up your agent to refresh the index periodically using the tools provided to keep your RAG answers accurate.

Your agent fetches the live state during the next query. Since it is connected via an MCP server, it always uses the most recent information available.

Use it with your favorite AI tools

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