How do I request FareHarbor API access?

API access is not public. You must email support@fareharbor.com with your business use case to receive your unique App Key and User Key.

Is there a sandbox environment for testing?

Yes, FareHarbor provides a demo environment at demo.fareharbor.com. You can use this host with your sandbox credentials to test bookings safely.

What is a 'shortname' in FareHarbor?

A shortname is a unique identifier for a specific tour operator or company on the FareHarbor platform. You can find it in the company metadata or dashboard.

FareHarbor MCP Server for CrewAI 11 tools — connect in under 2 minutes

Name: FareHarbor
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 11 Tools Framework

Connect your CrewAI agents to FareHarbor through Vinkius, pass the Edge URL in the `mcps` parameter and every FareHarbor tool is auto-discovered at runtime. No credentials to manage, no infrastructure to maintain.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

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Vinkius supports streamable HTTP and SSE.

python

from crewai import Agent, Task, Crew

agent = Agent(
    role="FareHarbor Specialist",
    goal="Help users interact with FareHarbor effectively",
    backstory=(
        "You are an expert at leveraging FareHarbor tools "
        "for automation and data analysis."
    ),
    # Your Vinkius token. get it at cloud.vinkius.com
    mcps=["https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"],
)

task = Task(
    description=(
        "Explore all available tools in FareHarbor "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 11 available tools "
        "and what they can do."
    ),
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

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* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About FareHarbor MCP Server

Connect your FareHarbor affiliate or partner account to any AI agent and take full control of your tour and activity bookings through natural conversation.

When paired with CrewAI, FareHarbor becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call FareHarbor tools autonomously, one agent queries data, another analyzes results, a third compiles reports, all orchestrated through Vinkius with zero configuration overhead.

What you can do

Operator Discovery — List all authorized tour companies and operators in your network
Live Inventory Access — Query specific tours (items) and check real-time availability for any date or range
Seamless Booking — Create new bookings with customer details and ticket types directly from the cloud
Availability Inspection — Fetch granular details for specific time slots including pricing and remaining capacity
Booking Management — List recent bookings and retrieve detailed status by UUID flawlessy
User Context — Verify your API application and user identity credentials through the agent

The FareHarbor MCP Server exposes 11 tools through the Vinkius. Connect it to CrewAI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect FareHarbor to CrewAI via MCP

Follow these steps to integrate the FareHarbor MCP Server with CrewAI.

Install CrewAI

Run pip install crewai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com

Customize the agent

Adjust the role, goal, and backstory to fit your use case

Run the crew

Run python crew.py. CrewAI auto-discovers 11 tools from FareHarbor

Why Use CrewAI with the FareHarbor MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with FareHarbor through the Model Context Protocol.

Multi-agent collaboration lets you decompose complex workflows into specialized roles, one agent researches, another analyzes, a third generates reports, each with access to MCP tools

CrewAI's native MCP integration requires zero adapter code: pass Vinkius Edge URL directly in the `mcps` parameter and agents auto-discover every available tool at runtime

Built-in task delegation and shared memory mean agents can pass context between steps without manual state management, enabling multi-hop reasoning across tool calls

Sequential and hierarchical crew patterns map naturally to real-world workflows: enumerate subdomains → analyze DNS history → check WHOIS records → compile findings into actionable reports

FareHarbor + CrewAI Use Cases

Practical scenarios where CrewAI combined with the FareHarbor MCP Server delivers measurable value.

Automated multi-step research: a reconnaissance agent queries FareHarbor for raw data, then a second analyst agent cross-references findings and flags anomalies. all without human handoff

Scheduled intelligence reports: set up a crew that periodically queries FareHarbor, analyzes trends over time, and generates executive briefings in markdown or PDF format

Multi-source enrichment pipelines: chain FareHarbor tools with other MCP servers in the same crew, letting agents correlate data across multiple providers in a single workflow

Compliance and audit automation: a compliance agent queries FareHarbor against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

FareHarbor MCP Tools for CrewAI (11)

These 11 tools become available when you connect FareHarbor to CrewAI via MCP:

create_booking

Create a new booking for a tour or activity

get_availability

Get details for a specific availability slot

get_booking

Get details for a specific booking by UUID

get_item_details

Get details for a specific tour or activity

get_me

Get current API user/affiliate identity

list_availabilities_by_date

List available slots for an item on a specific date

list_availabilities_by_range

List available slots for an item within a date range

list_bookings

List recent bookings for a company

list_companies

List all companies (operators) authorized for booking

list_items

List all tours or activities (items) for a specific company

list_lodgings

List lodging/pickup locations for a company

Example Prompts for FareHarbor in CrewAI

Ready-to-use prompts you can give your CrewAI agent to start working with FareHarbor immediately.

"List the tour companies I can book for."

"Check availability for the Whale Watching tour tomorrow."

"Show me the details for booking UUID abc-123."

Troubleshooting FareHarbor MCP Server with CrewAI

Common issues when connecting FareHarbor to CrewAI through the Vinkius, and how to resolve them.

MCP tools not discovered

Ensure the Edge URL is correct. CrewAI connects lazily when the crew starts. check console output.

Agent not using tools

Make the task description specific. Instead of "do something", say "Use the available tools to list contacts".

Timeout errors

CrewAI has a 10s connection timeout by default. Ensure your network can reach the Edge URL.

Rate limiting or 429 errors

Vinkius enforces per-token rate limits. Check your subscription tier and request quota in the dashboard. Upgrade if you need higher throughput.

FareHarbor + CrewAI FAQ

Common questions about integrating FareHarbor MCP Server with CrewAI.

How does CrewAI discover and connect to MCP tools?

CrewAI connects to MCP servers lazily. when the crew starts, each agent resolves its MCP URLs and fetches the tool catalog via the standard tools/list method. This means tools are always fresh and reflect the server's current capabilities. No tool schemas need to be hardcoded.

Can different agents in the same crew use different MCP servers?

Yes. Each agent has its own mcps list, so you can assign specific servers to specific roles. For example, a reconnaissance agent might use a domain intelligence server while an analysis agent uses a vulnerability database server.

What happens when an MCP tool call fails during a crew run?

CrewAI wraps tool failures as context for the agent. The LLM receives the error message and can decide to retry with different parameters, fall back to a different tool, or mark the task as partially complete. This resilience is critical for production workflows.

Can CrewAI agents call multiple MCP tools in parallel?

CrewAI agents execute tool calls sequentially within a single reasoning step. However, you can run multiple agents in parallel using process=Process.parallel, each calling different MCP tools concurrently. This is ideal for workflows where separate data sources need to be queried simultaneously.

Can I run CrewAI crews on a schedule (cron)?

Yes. CrewAI crews are standard Python scripts, so you can invoke them via cron, Airflow, Celery, or any task scheduler. The crew.kickoff() method runs synchronously by default, making it straightforward to integrate into existing pipelines.