How to Use the Fieldly MCP in LlamaIndex

Q: How do I connect the Fieldly MCP Server to LlamaIndex?

Install llama-index-tools-mcp and initialize the BasicMCPClient with your Vinkius URL. Convert the connection to tools using McpToolSpec and pass them to your LlamaIndex FunctionAgent.

Q: Can LlamaIndex index live Fieldly bookings?

Yes, your LlamaIndex pipeline can periodically call listbookings to refresh its vector store. This ensures your search agent always has access to the latest field schedules.

Q: How does LlamaIndex prevent hallucinations with Fieldly data?

By forcing the agent to use getworkitem and getinvoice before answering, LlamaIndex grounds every response in real-time data retrieved directly from your Fieldly account.

Q: Can I filter which Fieldly tools LlamaIndex can access?

Yes, you can use the allowedtools filter in the MCP tool spec to restrict your agent to specific tools like listarticles or listcustomers using the MCP tool spec.

Build RAG pipelines that index your Fieldly work items and bookings directly into LlamaIndex search.

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Connect Fieldly MCP to LlamaIndex

Create your Vinkius account to connect Fieldly 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 Fieldly with LlamaIndex

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Index Fieldly work items into LlamaIndex

The `list_work_items` tool pulls your active construction jobs so LlamaIndex can ingest them into your vector database. Your agent queries this local index to find patterns in past projects without hitting the live API every time. This speeds up retrieval and grounds your agent's answers in actual site data. You can query historical jobs using semantic search. LlamaIndex takes the output of `get_work_item` and creates embeddings, making it easy to find similar past projects when planning new jobs.

Query live scheduling data via this MCP Server

The `list_bookings` tool supplies real-time scheduling data directly to your LlamaIndex RAG application. Your agent queries this live feed to answer natural language questions about which crews are on which sites today. This eliminates manual calendar checks. The tool feeds raw scheduling structures into the indexer. LlamaIndex processes these bookings so your query engine can cross-reference field availability with incoming client requests.

Ground financial answers in invoice data

The `list_invoices` tool extracts billing records to ground your financial queries in hard data. Your agent calls `get_invoice` to pull specific line items, preventing hallucinated balances when discussing client accounts. This ensures absolute accuracy for your back-office operations. By indexing these financial documents, LlamaIndex lets you run complex analytics over your payment history. You can search for outstanding invoices by customer name or job type using natural language.

Setup guide

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

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Common questions about Fieldly MCP in LlamaIndex

Install `llama-index-tools-mcp` and initialize the `BasicMCPClient` with your Vinkius URL. Convert the connection to tools using `McpToolSpec` and pass them to your LlamaIndex `FunctionAgent`.

Yes, your LlamaIndex pipeline can periodically call `list_bookings` to refresh its vector store. This ensures your search agent always has access to the latest field schedules.

By forcing the agent to use `get_work_item` and `get_invoice` before answering, LlamaIndex grounds every response in real-time data retrieved directly from your Fieldly account.

Yes, you can use the `allowed_tools` filter in the MCP tool spec to restrict your agent to specific tools like `list_articles` or `list_customers` using the MCP tool spec.

Your work items and inventory articles are never stored on external servers. Vinkius runs the MCP server in a secure, ephemeral sandbox, passing data directly to your local LlamaIndex instance over encrypted connections.

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