How to Use the FareHarbor MCP in LlamaIndex

Q: How do I index FareHarbor tour descriptions in LlamaIndex?

You run listitems to pull the catalog via the MCP Server and load the results into a LlamaIndex document index. Your agent can then run semantic search over the descriptions before calling getitemdetails for live pricing.

Q: Can LlamaIndex check live FareHarbor availability before answering?

Yes, your RAG query engine can run getavailability to fetch real-time slot counts. This keeps your agent's answers grounded in active inventory instead of stale, cached index data.

Q: How do I handle multiple tour operators in LlamaIndex?

Your agent runs listcompanies to get the list of active operators. It then indexes each operator's inventory separately so users can search across multiple brands in LlamaIndex.

Index live FareHarbor tour data into LlamaIndex vector stores for fast, context-rich agent bookings.

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Connect FareHarbor MCP to LlamaIndex

Create your Vinkius account to connect FareHarbor 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 FareHarbor with LlamaIndex

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Index active tour catalogs for semantic search

The `list_items` tool pulls active tour descriptions and metadata directly into your LlamaIndex document store. Your agent queries this indexed inventory using semantic search, matching customer requests to actual tour details without hitting the live API for every question. When a customer asks for outdoor activities, LlamaIndex searches the indexed item metadata and uses `get_item_details` to pull the exact pricing. You bypass slow, repetitive API calls while keeping your agent's responses grounded in real catalog data.

Ground RAG workflows with live MCP Server availability

This MCP Server provides `list_availabilities_by_range` to inject real-time scheduling data directly into your LlamaIndex query engine. The agent combines static tour documents with live calendar slots to answer availability questions with zero hallucinations. By feeding `get_availability` outputs into your index, your RAG pipeline gets instant access to current spot counts. Your agent never suggests a tour that is already fully booked.

Build queryable booking histories in LlamaIndex

The `list_bookings` tool retrieves recent reservation records so LlamaIndex can index your operational history for quick search. Your agent searches past bookings to answer customer support queries about pickup times or passenger counts. If a customer asks where they should wait, the agent pulls lodging data using `list_lodgings` and matches it against the booking details. You get a unified search interface across your entire tour operation history.

Setup guide

Set up FareHarbor 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 FareHarbor 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 FareHarbor tools.",
)
response = await agent.run("List recent FareHarbor 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 FareHarbor. 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 FareHarbor MCP in LlamaIndex

You run `list_items` to pull the catalog via the MCP Server and load the results into a LlamaIndex document index. Your agent can then run semantic search over the descriptions before calling `get_item_details` for live pricing.

Yes, your RAG query engine can run `get_availability` to fetch real-time slot counts. This keeps your agent's answers grounded in active inventory instead of stale, cached index data.

Your agent runs `list_companies` to get the list of active operators. It then indexes each operator's inventory separately so users can search across multiple brands in LlamaIndex.

No, you can use any vector store supported by LlamaIndex. The MCP tools simply supply the structured JSON data that you choose to index.

Yes, the server only reads the booking records you request using `get_booking`. Your customer names and reservation details are processed in memory and never stored on the Vinkius hosting platform.

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