Routific MCP Server for Pydantic AI 10 tools — connect in under 2 minutes
Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Routific 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 Routific "
"(10 tools)."
),
)
result = await agent.run(
"What tools are available in Routific?"
)
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 Routific MCP Server
Connect your conversational assistant directly to Routific, a premier logistics scaling platform. This integration seamlessly turns your AI into an advanced delivery dispatcher, allowing you to build multi-stop route solutions securely, manage outstanding delivery jobs, and proactively push dispatch tasks directly to drivers' mobile apps natively in one window.
Pydantic AI validates every Routific tool response against typed schemas, catching data inconsistencies at build time. Connect 10 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.
What you can do
- Automate VRP Computations — Submit basic logistics parameters (
solve_standalone_vrp) or delegate massive multi-depot configurations organically (solve_async_vrp_long) and query asynchronous status returns effortlessly (poll_async_solution). - Control Saas Delivery Jobs — Tell the AI to actively audit outstanding orders (
list_platform_jobs) or create fresh delivery injections accurately handling order constraints and priorities directly into the system (create_saas_job,update_saas_job). - Assemble & Publish Timelines — Review the resulting stop-by-stop ETAs securely calculated by algorithms natively inside the interface (
get_route_timeline). Once completely satisfied, simply push the finalized route natively to the targeted driver's phone with an organic command (publish_route_to_driver).
The Routific MCP Server exposes 10 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 Routific to Pydantic AI via MCP
Follow these steps to integrate the Routific 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 10 tools from Routific with type-safe schemas
Why Use Pydantic AI with the Routific MCP Server
Pydantic AI provides unique advantages when paired with Routific 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 Routific integration code
Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors
Dependency injection system cleanly separates your Routific connection logic from agent behavior for testable, maintainable code
Routific + Pydantic AI Use Cases
Practical scenarios where Pydantic AI combined with the Routific MCP Server delivers measurable value.
Type-safe data pipelines: query Routific with guaranteed response schemas, feeding validated data into downstream processing
API orchestration: chain multiple Routific tool calls with Pydantic validation at each step to ensure data integrity end-to-end
Production monitoring: build validated alert agents that query Routific and output structured, schema-compliant notifications
Testing and QA: use Pydantic AI's dependency injection to mock Routific responses and write comprehensive agent tests
Routific MCP Tools for Pydantic AI (10)
These 10 tools become available when you connect Routific to Pydantic AI via MCP:
cancel_saas_job
This action is irreversible. Cancels and deletes a delivery job from the platform
create_platform_route
Creates a new route plan in the platform
create_saas_job
Provide a JSON object with order details. Creates a new delivery job in the platform
get_route_timeline
Retrieves the stop-by-stop timeline for a route
list_platform_jobs
Lists all delivery jobs in the Routific platform
poll_async_solution
Polls the status of an asynchronous VRP job
publish_route_to_driver
Publishes a route to the driver's mobile app
solve_async_vrp_long
Returns a job ID for polling. Submits a large Vehicle Routing Problem for asynchronous solving
solve_standalone_vrp
Provide a JSON object with visits, fleet, and options. Solves a standalone Vehicle Routing Problem synchronously
update_saas_job
Updates an existing delivery job
Example Prompts for Routific in Pydantic AI
Ready-to-use prompts you can give your Pydantic AI agent to start working with Routific immediately.
"List all current delivery jobs pending in the platform right now."
"Generate a standalone route resolving 4 pending visits."
"Publish the finalized route to the designated driver's mobile app."
Troubleshooting Routific MCP Server with Pydantic AI
Common issues when connecting Routific to Pydantic AI through the Vinkius, and how to resolve them.
MCPServerHTTP not found
pip install --upgrade pydantic-aiRoutific + Pydantic AI FAQ
Common questions about integrating Routific 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 Routific 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 Routific to Pydantic AI
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.