How to Use the WorkflowMax MCP in LlamaIndex

Q: How does WorkflowMax and LlamaIndex improve project auditing?

By indexing timesheet entries using listtimesheets, you create a permanent knowledge base. You can query historical work logs years later, getting grounded answers about resource allocation.

Q: Can I track client growth using WorkflowMax and LlamaIndex?

Yes. You can index the results of listclients over time. This allows you to query knowledge about overall company growth or which client has generated the most records.

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Connect WorkflowMax MCP to LlamaIndex

Create your Vinkius account to connect WorkflowMax 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 WorkflowMax with LlamaIndex

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Indexing Client Relationships with MCP Server

When you execute `create_client` and then follow up by running `get_client_contact`, the resulting client ID and contact details become searchable knowledge. You're not just calling a tool; you're adding verified business context to your index. Future queries can retrieve accurate data points, like 'What is the primary email for Client XYZ?'—all grounded in the actual API output from WorkflowMax.

Querying Historical Job Data with LlamaIndex

The `list_jobs` tool provides a snapshot of active projects. By indexing this data, you can query historical records and get immediate answers about project status without writing complex filters. This turns raw list output into usable knowledge. Need to know the scope? You combine job details with task information from `list_tasks`. Your agent builds an answer based on both indexed sources.

Semantic Search for Timesheets via WorkflowMax

Every time you call `create_timesheet`, that record—including the hours and associated job UUID—gets indexed. This means you can ask complex questions like, 'Show me all work done on Client A's project in Q3.' LlamaIndex pulls data from `list_timesheets` and contextualizes it, giving answers based on meaning, not just keywords.

Setup guide

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

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Common questions about WorkflowMax MCP in LlamaIndex

By indexing timesheet entries using `list_timesheets`, you create a permanent knowledge base. You can query historical work logs years later, getting grounded answers about resource allocation.

It uses structured business data from all tools: Client details (`get_client`), Job records (`get_job`), Task definitions, and Timesheet entries. Everything becomes part of the searchable index.

Yes. You can index the results of `list_clients` over time. This allows you to query knowledge about overall company growth or which client has generated the most records.

Since the tool output is indexed into a vector store, all data—from `create_client` to timesheets—is retained as searchable knowledge, eliminating reliance on temporary API calls.

This server manages Client IDs, Contact Details, Job UUIDs, Task descriptions, and Timesheet hours. It builds a comprehensive knowledge graph of all client work.

Use it with your favorite AI tools

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