How to Use the Beeline MCP in LlamaIndex

Q: Can LlamaIndex index live Beeline timesheet data?

Yes, you can run a pipeline that calls listtimesheets and indexes the resulting payloads into a vector store. This lets you run semantic queries over your historical contractor hours.

Q: How does Beeline search work with LlamaIndex retrievers?

The agent uses searchrequisitions to locate candidates, then passes the results to a retriever node. This combines live API search with your local vector index for hybrid retrieval.

Q: Does this setup support the includeresources flag?

Yes, the LlamaIndex integration supports includeresources=True. This allows your agent to fetch raw data structures directly using Beeline MCP tool parameters.

Index your Beeline workforce data into LlamaIndex vector stores to query active assignments and timesheets using our MCP Server.

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Connect Beeline MCP to LlamaIndex

Create your Vinkius account to connect Beeline 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 Beeline with LlamaIndex

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Indexing Beeline MCP Server data for LlamaIndex RAG

The Beeline MCP Server provides `list_assignments` and `list_requisitions` to feed your LlamaIndex vector store with live workforce metadata. Your pipeline indexes these active records, turning raw contractor details into searchable document nodes. When you query your index, LlamaIndex pulls the latest assignment data instead of relying on outdated training weights. This ensures your agent answers questions about current contractor headcounts using actual, live API payloads.

Semantic search across open job requisitions

The `search_requisitions` tool retrieves active job postings while `get_requisition` extracts the detailed requirements of each opening. LlamaIndex indexes these descriptions, enabling semantic search across your entire requisition catalog. Instead of matching exact keywords, users can ask your agent for roles requiring specific experience profiles. The engine searches the vectorized requisition data to return the most relevant open positions.

Historical timesheet and expense analysis

The `list_timesheets` tool retrieves past billing periods, while `list_expenses` pulls itemized contractor costs into your index. LlamaIndex structures these financial records into a queryable knowledge base for budget tracking. You can query the index to find anomalies in billing or compare seasonal spending patterns. The system pulls the exact timesheet details directly from the VMS database to ground every financial response.

Setup guide

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

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Common questions about Beeline MCP in LlamaIndex

Install llama-index-tools-mcp and initialize the BasicMCPClient with your Vinkius endpoint. Use McpToolSpec to load the tools and pass them to your FunctionAgent.

Yes, you can run a pipeline that calls list_timesheets and indexes the resulting payloads into a vector store. This lets you run semantic queries over your historical contractor hours.

The agent uses search_requisitions to locate candidates, then passes the results to a retriever node. This combines live API search with your local vector index for hybrid retrieval.

Yes, the LlamaIndex integration supports include_resources=True. This allows your agent to fetch raw data structures directly using Beeline MCP tool parameters.

Yes, your sensitive workforce rates and assignment details are isolated. Vinkius manages the credential handshake, and data is transferred directly to your local LlamaIndex vector store over encrypted channels.

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