Can I check the status of a specific job requisition from the agent?

Yes! Use the `get_requisition` tool with the Requisition ID. Your agent will fetch the latest status and metadata directly from the Beeline cloud.

How do I list all active assignments for my department?

Simply ask the agent to `list_assignments`. It will retrieve the current work orders and assignments linked to your account for your review.

Does the integration support approving timesheets directly?

Currently, the toolset is focused on querying and auditing (Read-Only). You can list and inspect timesheets, but approvals must be performed within the Beeline VMS platform for security compliance.

Beeline MCP Server for LlamaIndex 10 tools — connect in under 2 minutes

Name: Beeline
Availability: InStock
Author: Vinkius

docs.llamaindex.ai/en/stable/module_guides/tools/mcp

Built by Vinkius GDPR 10 Tools Framework

LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Beeline as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

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

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
    mcp_tool_spec = McpToolSpec(client=mcp_client)
    tools = await mcp_tool_spec.to_tool_list_async()

    agent = FunctionAgent(
        tools=tools,
        llm=OpenAI(model="gpt-4o"),
        system_prompt=(
            "You are an assistant with access to Beeline. "
            "You have 10 tools available."
        ),
    )

    response = await agent.run(
        "What tools are available in Beeline?"
    )
    print(response)

asyncio.run(main())

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* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Beeline MCP Server

Connect your Beeline Vendor Management System (VMS) account to any AI agent and orchestrate your contingent workforce operations through natural conversation.

LlamaIndex agents combine Beeline tool responses with indexed documents for comprehensive, grounded answers. Connect 10 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.

What you can do

Assignment Oversight — List and inspect active work assignments to monitor external talent deployment.
Requisition Management — Query job requisitions and search for open postings within your organization.
Time & Expense Tracking — Retrieve submitted timesheets and expense reports for auditing and approval workflows.
Supplier Management — List and verify the vendors and suppliers linked to your Beeline account.
User Auditing — Retrieve account profile information to ensure correct system access.

The Beeline MCP Server exposes 10 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Beeline to LlamaIndex via MCP

Follow these steps to integrate the Beeline MCP Server with LlamaIndex.

Install dependencies

Run pip install llama-index-tools-mcp llama-index-llms-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 10 tools from Beeline

Why Use LlamaIndex with the Beeline MCP Server

LlamaIndex provides unique advantages when paired with Beeline through the Model Context Protocol.

Data-first architecture: LlamaIndex agents combine Beeline tool responses with indexed documents for comprehensive, grounded answers

Query pipeline framework lets you chain Beeline tool calls with transformations, filters, and re-rankers in a typed pipeline

Multi-source reasoning: agents can query Beeline, a vector store, and a SQL database in a single turn and synthesize results

Observability integrations show exactly what Beeline tools were called, what data was returned, and how it influenced the final answer

Beeline + LlamaIndex Use Cases

Practical scenarios where LlamaIndex combined with the Beeline MCP Server delivers measurable value.

Hybrid search: combine Beeline real-time data with embedded document indexes for answers that are both current and comprehensive

Data enrichment: query Beeline to augment indexed data with live information before generating user-facing responses

Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Beeline for fresh data

Analytical workflows: chain Beeline queries with LlamaIndex's data connectors to build multi-source analytical reports

Beeline MCP Tools for LlamaIndex (10)

These 10 tools become available when you connect Beeline to LlamaIndex via MCP:

get_assignment

Get details of a specific assignment

get_requisition

Get details of a job requisition

get_timesheet

Get details of a specific timesheet

get_user_info

Get Beeline user profile

list_assignments

List active work assignments

list_expenses

List expense reports

list_requisitions

List job requisitions

list_suppliers

List vendors/suppliers

list_timesheets

List submitted timesheets

search_requisitions

Search job requisitions by keyword

Example Prompts for Beeline in LlamaIndex

Ready-to-use prompts you can give your LlamaIndex agent to start working with Beeline immediately.

"List all active assignments in Beeline."

"Search for open requisitions matching 'React'."

"Show me recent timesheets that need review."

Troubleshooting Beeline MCP Server with LlamaIndex

Common issues when connecting Beeline to LlamaIndex through the Vinkius, and how to resolve them.

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

Beeline + LlamaIndex FAQ

Common questions about integrating Beeline MCP Server with LlamaIndex.

How does LlamaIndex connect to MCP servers?

Use the MCP client adapter to create a connection. LlamaIndex discovers all tools and wraps them as query engine tools compatible with any LlamaIndex agent.

Can I combine MCP tools with vector stores?

Yes. LlamaIndex agents can query Beeline tools and vector store indexes in the same turn, combining real-time and embedded data for grounded responses.