Arrivy MCP Server for Pydantic AI 9 tools — connect in under 2 minutes
Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Arrivy through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
agent = Agent(
model="openai:gpt-4o",
mcp_servers=[server],
system_prompt=(
"You are an assistant with access to Arrivy "
"(9 tools)."
),
)
result = await agent.run(
"What tools are available in Arrivy?"
)
print(result.data)
asyncio.run(main())
* 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 Arrivy MCP Server
The Arrivy MCP Server empowers your AI agent to coordinate field operations and last-mile delivery directly from your workspace. Seamlessly manage your mobile workforce, track job progress, and engage with customers using natural language.
Pydantic AI validates every Arrivy tool response against typed schemas, catching data inconsistencies at build time. Connect 9 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.
Key Features
- Task Orchestration — List, create, and update service tasks or delivery jobs with real-time status tracking.
- Crew Management — Monitor field personnel and resource assignments to ensure efficient job allocation.
- Customer Engagement — Manage customer records and sync service history for better communication.
- Location Tracking — Access real-time location data and ETAs for your field technicians and delivery drivers.
- Digital Workflow — Access data captured in the field, including forms, photos, and status updates.
- Seamless Integration — Connect your Arrivy operations with your AI-assisted project management and support workflows.
Benefits for Teams
- Operations Managers — Quickly audit active jobs and crew statuses without leaving your AI dashboard.
- Dispatchers — Use AI to quickly create and assign new tasks based on customer requests.
- Customer Success — Retrieve job history and ETAs instantly to provide accurate updates to clients.
The Arrivy MCP Server exposes 9 tools through the Vinkius. Connect it to Pydantic AI 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 Arrivy to Pydantic AI via MCP
Follow these steps to integrate the Arrivy MCP Server with Pydantic AI.
Install Pydantic AI
Run pip install pydantic-ai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 9 tools from Arrivy with type-safe schemas
Why Use Pydantic AI with the Arrivy MCP Server
Pydantic AI provides unique advantages when paired with Arrivy through the Model Context Protocol.
Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application
Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your Arrivy integration code
Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors
Dependency injection system cleanly separates your Arrivy connection logic from agent behavior for testable, maintainable code
Arrivy + Pydantic AI Use Cases
Practical scenarios where Pydantic AI combined with the Arrivy MCP Server delivers measurable value.
Type-safe data pipelines: query Arrivy with guaranteed response schemas, feeding validated data into downstream processing
API orchestration: chain multiple Arrivy tool calls with Pydantic validation at each step to ensure data integrity end-to-end
Production monitoring: build validated alert agents that query Arrivy and output structured, schema-compliant notifications
Testing and QA: use Pydantic AI's dependency injection to mock Arrivy responses and write comprehensive agent tests
Arrivy MCP Tools for Pydantic AI (9)
These 9 tools become available when you connect Arrivy to Pydantic AI via MCP:
create_customer
Create a new customer record
create_task
Create a new service task in Arrivy
get_account_check
Verify Arrivy account connection
get_task
Get details for a specific task
list_crews
List all field crews and personnel
list_customers
List all customers in the system
list_locations
List all tracked locations
list_tasks
List all service tasks in Arrivy
update_task
Update an existing service task
Example Prompts for Arrivy in Pydantic AI
Ready-to-use prompts you can give your Pydantic AI agent to start working with Arrivy immediately.
"List all scheduled tasks for today in Arrivy."
"Create a new task 'Emergency Leak Repair' at '123 Maple St'."
"Show me the status of task ID 'T12345'."
Troubleshooting Arrivy MCP Server with Pydantic AI
Common issues when connecting Arrivy to Pydantic AI through the Vinkius, and how to resolve them.
MCPServerHTTP not found
pip install --upgrade pydantic-aiArrivy + Pydantic AI FAQ
Common questions about integrating Arrivy MCP Server with Pydantic AI.
How does Pydantic AI discover MCP tools?
MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.Does Pydantic AI validate MCP tool responses?
Can I switch LLM providers without changing MCP code?
Connect Arrivy with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Arrivy to Pydantic AI
Get your token, paste the configuration, and start using 9 tools in under 2 minutes. No API key management needed.