How to Use the Fieldfolio MCP in LlamaIndex

Q: How do I filter which MCP tools my agent can access?

Use the allowedtools filter when initializing your MCP tool specification. This lets you restrict your agent to read-only tools like inventory checks while blocking write tools.

Index your Fieldfolio catalog and orders into LlamaIndex to query live wholesale data without hallucinations.

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Connect Fieldfolio MCP to LlamaIndex

Create your Vinkius account to connect Fieldfolio 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 Fieldfolio with LlamaIndex

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Index wholesale data into LlamaIndex

Feeding `list_products` and `search_catalog` data directly into LlamaIndex vector stores grounds your agent in live wholesale catalog details using our MCP Server. Your agent queries this index to retrieve exact product specifications, category structures from `list_categories`, and pricing details. This eliminates hallucinated product details. Your RAG applications query live wholesale data, ensuring your sales team gets accurate specs and stock levels every time.

Context-aware order analysis

Analyzing history with `list_orders` and `get_order` provides the raw transaction history needed to build searchable order indexes in LlamaIndex. Your agent parses past orders, matches them with customer profiles from `get_customer`, and builds a queryable history. Instead of running complex SQL queries, you ask your agent which retail clients ordered specific items last quarter. The agent pulls the data, indexes it, and returns the correct summary.

Ground your RAG pipeline with Fieldfolio

Querying `list_inventory` and `update_inventory` grounds your LlamaIndex agent's responses in real-time warehouse reality. When a customer asks if an item is available, the agent calls the inventory tool to check the exact count before answering. You configure the tool specification to load these tools asynchronously. This keeps your query pipelines fast, even when fetching live data from multiple warehouse locations.

Setup guide

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

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Fieldfolio. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Fieldfolio MCP in LlamaIndex

Install the MCP tool package and initialize the basic client with your Vinkius URL. Convert the server tools using the tool specification class and pass them to your function agent.

You can configure LlamaIndex to cache responses from the product and category tools in a local vector store. This reduces API calls and speeds up semantic search queries.

Yes, by exposing customer tools to your agent, LlamaIndex can index customer profiles alongside their order history for deep relationship analysis.

Use the allowed_tools filter when initializing your MCP tool specification. This lets you restrict your agent to read-only tools like inventory checks while blocking write tools.

Your product inventory quantities and stock levels are processed entirely within secure, ephemeral V8 isolates. No warehouse data is stored permanently or leaked outside your active execution environment.

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