How to Use the FareHarbor MCP in Pydantic AI

Get type-safe FareHarbor booking management in Pydantic AI.

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Connect FareHarbor MCP to Pydantic AI

Create your Vinkius account to connect FareHarbor to Pydantic AI 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 Pydantic AI

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Type-safe booking operations in Pydantic AI

Every tool call is validated against a Pydantic model at runtime. If `create_booking` returns unexpected data, the agent fails immediately, preventing silent data corruption. This ensures that your agent's state always matches the reality of your FareHarbor account. You get strong typing for every field without writing custom schemas.

Strict data validation for FareHarbor

Use `get_availability` with complete confidence. The framework checks the response structure against your defined models, ensuring your agent only works with valid, expected data. This approach removes the risk of hallucinations in the booking data. Your agent treats every returned value as a verified source of truth.

Manage company resources in Pydantic AI

Fetch company and lodging lists using `list_companies` and `list_lodgings`. The output is automatically coerced into your Pydantic models for immediate use in your code. This makes it trivial to integrate tour metadata into your business logic. You write less boilerplate and get more reliability in your agent workflows.

Setup guide

Set up FareHarbor MCP in Pydantic AI

Prerequisites

Python 3.10+ installed
pydantic-ai-slim[fastmcp] package
Active Vinkius subscription with a valid endpoint token

1

Install Pydantic AI with FastMCP
Run pip install "pydantic-ai-slim[fastmcp]". The FastMCP toolset replaces the deprecated MCPServerHTTP class with full protocol support.
2

Configure the FastMCPToolset
Pass a JSON-style config dict to FastMCPToolset with your Vinkius URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports Streamable HTTP, SSE, and Stdio transports.
3

Create and run your agent
Pass the toolset to Agent(toolsets=[toolset]) and call agent.run(). Swap openai:gpt-4o for any supported model — Anthropic, Google, Mistral, or Groq.

agent.py

from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

toolset = FastMCPToolset({
    "mcpServers": {
        "fareharbor-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to FareHarbor tools.",
)

result = await agent.run("List recent FareHarbor transactions")
print(result.output)

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Common questions about FareHarbor MCP in Pydantic AI

It provides runtime validation for all tool responses. This stops your agent from processing malformed data and ensures your booking records remain clean.

The server provides the structure. You can define your Pydantic models to mirror the API schema, and the framework handles the validation automatically.

The server manages your data in an ephemeral sandbox. Your sensitive booking information is validated in memory and never stored long-term in the agent's context.

Yes, the server supports SSE and Streamable HTTP. Your agent can handle responses as they arrive, maintaining a responsive user experience.

Your agent will catch the validation error immediately if the response no longer matches your Pydantic model. This gives you a clear signal to update your schema.

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