How to Use the Worksuite MCP in LlamaIndex

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Connect Worksuite MCP to LlamaIndex

Create your Vinkius account to connect Worksuite 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 Worksuite with LlamaIndex

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Claude

Perplexity

Searchable Contract History

LlamaIndex indexes the results from `list_talents` and `get_talent`. You don't just read a list; you build a semantic index of every contractor profile. Your agent can then answer complex questions like, 'Which talent has worked on a finance project before?' This capability turns raw API data into deeply searchable knowledge, bypassing simple keyword matching.

Deep Project Context Retrieval

When you run `get_project` or `list_projects`, the output isn't forgotten. LlamaIndex indexes it into a vector store. You can then query past sessions and get answers grounded in that specific project data, even months later. This makes your application historical—you’re querying actual Worksuite records, not just remembering them.

Financial Record Querying

Need to check payment status? Use `list_payments` or `get_job` data. LlamaIndex indexes these financial details alongside project descriptions. You can ask questions like, 'What was paid for the Q3 marketing job opening?' and get a precise answer. This gives you a unified knowledge base combining operational tasks with concrete money movement.

Setup guide

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

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Common questions about Worksuite MCP in LlamaIndex

You pass the MCP tool specs to your FunctionAgent. The results from tools like `list_jobs` are indexed into a vector store, allowing for semantic search.

It turns live API data and documents into a unified index. You can query past MCP Server sessions or configurations and get answers grounded in actual data from Worksuite.

Yes. By including resources, your agent can combine live API data—like `list_projects` results—with documents to build a single, comprehensive queryable index.

This server touches payment and invoicing details. You can run queries against the indexed data from `list_invoices` and `list_payments` to understand financial history.

It's highly reliable. By indexing the tool output, you get answers that are grounded in the actual API responses from Worksuite, eliminating hallucinations.

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