How to Use the Bilflo MCP in LlamaIndex

Index your staffing records into LlamaIndex to query Bilflo data using natural language.

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Connect Bilflo MCP to LlamaIndex

Create your Vinkius account to connect Bilflo 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 Bilflo with LlamaIndex

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Perplexity

Semantic search for Bilflo in LlamaIndex

The `list_placements` and `list_direct_hires` tools output data that LlamaIndex maps into a vector store. You ask questions about your staffing history and get grounded answers. Stop guessing where records live. The agent pulls the relevant context from the index before writing its response.

Ground answers with live Bilflo data

Combine static documents with real-time `get_timecard` results. Your RAG application provides answers that reflect the current state of your payroll. Queries return facts, not guesses. The agent retrieves the specific record and cites the exact timecard ID found in the system.

Filter Bilflo tools with LlamaIndex

Use the allowed_tools filter to restrict which staffing data your index can access. You keep sensitive payroll data isolated from general query agents. Setup is straightforward using the tool spec interface. You pass the tools to your agent and define the scope of the knowledge base.

Setup guide

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

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Bilflo. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Bilflo MCP in LlamaIndex

Use the MCP tool spec to pull data into your index. Once indexed, your agent searches through the staffing records using semantic queries.

It can if you index the records first. Your agent compares the retrieved timecard data against your historical benchmarks.

Your staffing data remains encrypted at rest and in transit. The Vinkius sandbox ensures that only your authorized agent accesses these records.

Yes, you can refresh the index by calling the list tools periodically. This keeps your staffing knowledge base up to date.

We only expose the specific fields required by your tools. The Vinkius architecture enforces strict isolation so your PII never enters the training set.

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