How to Use the Deputy MCP in LlamaIndex

Index your Deputy workforce data into LlamaIndex for semantic search and grounded answers across your entire organization.

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Connect Deputy MCP to LlamaIndex

Create your Vinkius account to connect Deputy 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 Deputy with LlamaIndex

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Index Deputy data for LlamaIndex

Turn `list_workforce_employees` into a searchable vector index. LlamaIndex stores this information so your agent can answer questions about staff long after the API call finishes. It grounds your agent in real-time data. You don't get stale responses because the index reflects the latest state of your business locations.

Query leave requests with LlamaIndex

Use `list_leave_requests` to populate your knowledge base with time-off history. Your agent performs semantic searches against these requests to find patterns. This approach lets you ask questions like which employees are off next month. The agent queries the index, not just the raw API.

Unified search for Deputy records

Combine `list_business_locations` with your internal documents in a single index. Your agent treats Deputy entities like any other piece of knowledge. This removes the need for custom parsing. LlamaIndex handles the heavy lifting of turning tool outputs into useful context for your RAG pipeline.

Setup guide

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

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Common questions about Deputy MCP in LlamaIndex

Use the McpToolSpec class to wrap the Deputy server. Once you convert the tools to a list, pass them to your FunctionAgent to enable the indexing flow.

Yes. By including the leave data in your index, the agent performs semantic lookups to find relevant time-off information.

No. Because the tool outputs are indexed, the agent grounds its responses in the actual JSON returned by the Deputy API.

Data access is restricted to the specific tools you allow in your agent config. Vinkius maintains strict isolation for every request.

You trigger the update by calling the tool and refreshing your index. It gives you full control over when the agent fetches fresh info.

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