How do I create a Railway Personal Access Token?

Log in to the [**Railway Dashboard**](https://railway.com/dashboard), go to **Account Settings** > **Tokens**, and click **Create Token**. You can create a no-workspace token for broad access or a project-scoped token for limited access. Copy the token immediately — it won't be shown again.

Can I manage environment variables via the agent?

Yes! Use `list_variables` to see all variable names (values are hidden for security) for a service in an environment. Use `set_variable` to create or update a variable with a name and value, and `delete_variable` to remove one. You'll need the service_id and environment_id from the list tools.

What types of services does Railway support?

Railway supports any containerized service: web applications (Node.js, Python, Go, Rust, Java), databases (PostgreSQL, MySQL, Redis, MongoDB), message brokers and custom Docker images. Each service is deployed as an isolated container with its own environment variables, domains and persistent volumes.

How do I find my service_id and environment_id?

Use `list_projects` to get your project_id, then `list_environments` with that project_id to get the environment_id. Next, use `list_services` with the project_id (and optionally environment_id) to get the service_id. These IDs are required for variable, deployment and domain operations.

Railway Alternative MCP Server for Pydantic AI 11 tools — connect in under 2 minutes

Name: Railway Alternative
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 11 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Railway Alternative through the Vinkius and every tool is automatically validated against Pydantic schemas — catch errors at build time, not in production.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

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 Railway Alternative "
            "(11 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in Railway Alternative?"
    )
    print(result.data)

asyncio.run(main())

Fully ManagedVinkius Servers

60%Token savings

High SecurityEnterprise-grade

IAMAccess control

EU AI ActCompliant

DLPData protection

V8 IsolateSandboxed

Ed25519Audit chain

<40msKill switch

Stream every event to Splunk, Datadog, or your own webhook in real-time

* 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 Railway Alternative MCP Server

Connect your Railway account to any AI agent and take full control of your cloud deployments through natural conversation.

Pydantic AI validates every Railway Alternative tool response against typed schemas, catching data inconsistencies at build time. Connect 11 tools through the 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

Project Discovery — List all projects and retrieve their details including names, descriptions and timestamps
Environment Management — View all deployment environments (production, staging, development) per project
Service Inspection — List all services (containers, databases, plugins) within a project, optionally filtered by environment
Deployment Tracking — View deployment history with status (success, failed, deploying) for any service
Variable Management — List, set and delete environment variables for services in specific environments
Volume Audit — List persistent storage volumes with their sizes and associated services
Domain Management — Review custom domains and their SSL certificate status for any service

The Railway Alternative MCP Server exposes 11 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 Railway Alternative to Pydantic AI via MCP

Follow these steps to integrate the Railway Alternative 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 11 tools from Railway Alternative with type-safe schemas

Why Use Pydantic AI with the Railway Alternative MCP Server

Pydantic AI provides unique advantages when paired with Railway Alternative 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 Railway Alternative integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your Railway Alternative connection logic from agent behavior for testable, maintainable code

Railway Alternative + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the Railway Alternative MCP Server delivers measurable value.

Type-safe data pipelines: query Railway Alternative with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple Railway Alternative tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query Railway Alternative and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock Railway Alternative responses and write comprehensive agent tests

Railway Alternative MCP Tools for Pydantic AI (11)

These 11 tools become available when you connect Railway Alternative to Pydantic AI via MCP:

delete_variable

Provide the service_id, environment_id and variable name. WARNING: the variable will no longer be available to deployments after deletion. Delete an environment variable from a Railway service

get_project

Provide the project ID obtained from list_projects. Get details for a specific Railway project

get_viewer

Use this to verify which account the API token belongs to. Get current authenticated Railway user details

list_deployments

Each deployment has an ID, status (success, failed, deploying, removed), creation and update timestamps. Use the service_id from list_services. List deployments for a Railway service

list_domains

Each domain has an ID, the domain string and SSL status (verified, pending, failed). Use this to audit which services are accessible via custom URLs. List custom domains for a Railway service

list_environments

g. production, staging, development) configured within a specific Railway project. Each environment has its own set of services, variables and deployments. Use the project_id from list_projects. List environments in a Railway project

list_projects

Each project groups related services, environments and deployments together. Returns project ID, name, description and timestamps. Use this as the starting point for all Railway operations. List all Railway projects

list_services

Optionally filter by environment_id to see services in a specific environment only. Each service represents a deployable unit like a web app, API, database or Redis instance. List services in a Railway project

list_variables

Each variable has a name and scope (service, environment, project). Variable values are NOT returned for security — only names and scopes. Use service_id and environment_id from their respective list tools. List environment variables for a Railway service

list_volumes

Each volume has an ID, name, associated service ID and size in gigabytes. Volumes provide persistent storage that survives deployments and restarts. List persistent volumes in a Railway project

set_variable

Requires the service_id, environment_id, variable name and value. The variable will be available to all deployments of that service in the given environment. Set an environment variable for a Railway service

Example Prompts for Railway Alternative in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with Railway Alternative immediately.

"Show me all my Railway projects and their services."

"Set the DATABASE_URL variable for my api-web service in production."

"What's the deployment status of my api-web service?"

Troubleshooting Railway Alternative MCP Server with Pydantic AI

Common issues when connecting Railway Alternative to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Railway Alternative + Pydantic AI FAQ

Common questions about integrating Railway Alternative MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an 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?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer — your Railway Alternative MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.