Alpic MCP for AI. Control full infrastructure lifecycle from your agent.

Q: How do I check if my service is running correctly using getserverinfo?

You use getserverinfo to confirm operational status and list all currently exposed functions. This confirms the MCP is active before you try deploying or reading data.

Q: What if I need to test my code locally before I run deployenvironment?

Use gettunnelticket first. It generates a temporary URL and token, letting your agent test the service on your machine without affecting any live environments.

Q: Can I update an environment variable using addvariable if it already exists?

Yes. addvariable is designed to manage existing configuration keys, allowing you to securely overwrite or set new values for specific project/environment combinations.

Q: Which tool should I use to see all my running projects in one place? listprojects?

You must run listprojects first. It gives a complete overview of every service and its associated deployment status across your entire account.

Q: If I want to audit environment variables before deploying a change, should I use listvariables?

Yes, running listvariables shows you every variable key assigned to an environment. You can check if critical secrets, like API keys or database URLs, are present and configured correctly for your project.

Q: Before I run createproject, how do I find the correct team ID using listteams?

You must call listteams first. This returns all associated teams, giving you the necessary IDs to ensure your new MCP project is placed under the right organizational umbrella.

Q: If my deployment fails, should I check getdeployment or use getdeploymentlogs?

You need to use getdeploymentlogs. While getdeployment tells you if the process succeeded or failed, the logs provide the actual build output and stack traces necessary for deep debugging.

Claude

ChatGPT

Cursor

Gemini

Windsurf

VS Code

JetBrains

Vercel

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

Connect to your AI in seconds.

Alpic lets you manage your entire MCP infrastructure programmatically. You can build new projects, create isolated environments (dev, staging, prod), and deploy changes across all of them with a single command from your agent.

It also handles monitoring everything—from tracking latency to setting secure environment variables—so you don't have to manually check dashboards or manage release cycles anymore.

What your AI can do

Add variable

Adds a specific configuration value, like an API key or database URL, to an environment for the service.

Create environment

Sets up a completely isolated testing area (like dev or staging) for a project within Alpic.

Get tunnel ticket

Generates a temporary URL and token, allowing you to test the service locally without needing a full deployment cycle.

+ 15 more capabilities included

Project Structure Management

Create, read, update, and delete entire service projects, linking them directly to specific source code repositories.

Environment Isolation and Provisioning

Spin up completely separate deployment environments (dev, staging, prod) for a single project, ensuring changes in one area don't affect others.

Secure Configuration Management

Set or delete critical environment variables, like database URLs or API keys, and keep them stored securely across various environments.

Automated Deployment Workflow

Trigger new code versions to deploy instantly to any designated environment (dev, staging, prod) using one command.

Observability and Debugging

Fetch real-time deployment status, detailed logs for failures, or overall usage metrics like error rates and request counts.

Local Development Tunneling

Generate a temporary URL ticket that lets you test the service locally before pushing anything to any live environment.

Ask an AI about this

Included with Plan

Waiting for input…

AI Agent

Alpic: 18 Tools for Full MCP Management

These eighteen tools let you control every aspect of your deployed infrastructure, letting your agent manage project creation, environment variables, and continuous deployments.

Make your AI actually useful.

Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.

Start using Alpic on Vinkius

Add Variable

Adds a specific configuration value, like an API key or database URL, to an environment for the service.

Create Environment

Sets up a completely isolated testing area (like dev or staging) for a project...

Get Tunnel Ticket

Generates a temporary URL and token, allowing you to test the service locally...

Create Project

Establishes an entirely new, structured service project and links it to its required...

Delete Project

Permanently removes a specified service project from your entire infrastructure...

Delete Variable

Removes an old or unused configuration key and its value from a specific environment's settings.

Deploy Environment

Pushes the latest code version to a selected deployment target (dev, staging, or prod) for immediate testing.

Get Deployment Logs

Retrieves the full build output or startup logs for a given environment to find out...

Get Deployment

Checks the current status and detailed history of any specific deployment run using...

Get Project Analytics

Retrieves usage data, performance trends, and health metrics for a given MCP project...

Get Project

Fetches all configuration details and settings for a single service project before...

Get Server Info

Confirms the operational status of the service and lists all available functions exposed by the current setup.

List Environments

Lists all existing operational environments for a project, showing their current status and unique URLs.

List Projects

Provides an overview of every service project and its associated deployment status...

List Teams

Lists all organizational teams within your Alpic account, grouping different...

List Variables

Outputs a list of configured variable keys for an environment so you can audit the...

Publish To Registry

Makes your fully operational service project discoverable and visible to other users...

Update Project

Changes metadata for a project, such as renaming it or pointing it to a different source code branch without redeploying.

Security and governance baked right in.

Pick your AI client below to get set up. Just create a Vinkius account, subscribe, and you're instantly up and running. We handle the entire backend infrastructure, delivering out-of-the-box support for HTTPS Streamable, SSE, and OAuth2—zero messy routing required.

Claude AI

Open Claude Settings

Go to claude.ai, click your profile icon, then navigate to Customize → Connectors.

Add Custom Connector

Click the "+" button and select Add custom connector. Paste your Vinkius endpoint URL:

https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp

Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. For OAuth-protected servers, expand Advanced settings to add credentials.

Start a conversation

Open a new chat. The Alpic integration is available immediately — no restart needed.

Antigravity

Configure Agent Environment

Open your Antigravity agent's workspace configuration or mcp-servers.json file.

Bind the Endpoint

Add the Vinkius endpoint URL to your agent's MCP connections list:

"mcp_servers": {
  "alpic": {
    "serverUrl": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
  }
}

Provide your secure token in place of [YOUR_TOKEN_HERE] to ensure your agent requests are authenticated.

Execute

Start your Antigravity session. The agent will autonomously discover and utilize the Alpic tools with full Vinkius guardrails applied.

VS Code Copilot

⚡

One-Click Install (Recommended)

In your Vinkius Dashboard, simply click the Add to VS Code button for this server. We'll automatically configure your local workspace.

Or configure manually

Open MCP Settings

Open VS Code, press Ctrl/Cmd + Shift + P, and search for GitHub Copilot: MCP Servers.

Add Server Config

Add the Vinkius endpoint configuration to your mcp-servers.json file:

"alpic": {
  "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
}

Ensure you replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.

LangChain

Install Dependencies

Install the LangChain MCP adapters for your environment:

pip install langchain-mcp-adapters

Connect the Server

Use the SSEClient in LangChain to connect to the Vinkius managed endpoint:

from langchain_mcp_adapters.client import SSEClient

# Connect to Vinkius
client = SSEClient(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
tools = client.get_tools()

CrewAI

Define the Tool

Load the Vinkius MCP tools into your CrewAI agents:

from crewai import Agent
from mcp_crewai import MCPTool

# Connect securely to Vinkius
vinkius_tools = MCPTool(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

# Assign to Agent
researcher = Agent(
    role='Data Researcher',
    tools=vinkius_tools.get_all()
)

Execute Task

Run your CrewAI process. The agent will autonomously route tasks to the Vinkius managed server.

Choose How to Get Started

Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.

Build Your Own

Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.

Import from OpenAPI, Swagger, or YAML specs
Create Agent Skills with progressive disclosure
Deploy to edge with MCPFusion framework
Built in DLP, auth, and compliance on every call
Real time usage dashboard and cost metering
Publish to catalog or keep private

Start building

Make Your AI Do More

Start with Alpic, then connect any of our 5,100+ other servers whenever your AI needs more. One click, no limits.

Use this MCP plus 5,100+ others, all in one place
Add new capabilities to your AI anytime you want
Every connection is secured and compliant automatically
Track usage and costs across all your servers
Works with Claude, ChatGPT, Cursor, and more
New servers added to the catalog every week

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Alpic. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

VINKIUS INFRASTRUCTURE

Cloud Hosted

Managed infra

V8 Isolated

Sandboxed per request

Zero-Trust Proxy

No stored credentials

DLP Enforced

Policy on every call

GDPR Compliant

EU data residency

Token Compression

~60% cost reduction

Your data is protected. See how we built it.

Works with Claude, ChatGPT, Cursor, and more

The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.

This connection provides 18 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.

The tedious, multi-tab dashboard dance.

Right now, deploying a new feature means logging into three different dashboards: one for project setup, one for environment variables (where you have to copy and paste every single API key), and finally another to hit the 'Deploy' button. You spend 70% of your time clicking tabs and 30% actually doing work.

With this MCP, those steps vanish. Your agent handles the whole sequence—it creates the project structure, injects all variables using `add_variable`, and triggers the deployment via a single command call. The result is an automated pipeline that just works.

Managing your service lifecycle with Alpic MCP

Manual processes mean remembering to update variables across dev, staging, and prod separately. It means checking the status page 15 minutes after deployment just to see if it failed, wasting valuable time.

Now you can use `get_deployment` or `get_project_analytics` to get a single source of truth on performance and status. You stop guessing and start knowing—that’s the difference.

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Security Vinkius Trust Center ↗

SLA Service Level Agreement ↗

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What your AI can actually do with this

This MCP gives your agent full control over the entire lifecycle of your deployed services. Need to spin up a new feature? You create a dedicated project, link it to its git repo, and then set up specific environments for dev testing, staging verification, and production rollout. It handles that whole chain of commands—deploying code, managing secrets like API keys, and even publishing the service so others can find it.

When something breaks in prod, you don't just guess; your agent pulls logs and usage data to pinpoint the exact failure point. You control everything from project creation (create_project) to auditing resource usage (get_project_analytics). It’s built for teams that run multiple complex services and need continuous deployment without the human overhead.

If managing 5+ MCP instances sounds like a nightmare, connecting your AI client through Vinkius gives you one centralized place to manage all of it.

Built · Hosted · Managed by Vinkius Alpic MCP - Manage Infrastructure Deployment Lifecycle

Server ID 019d754c-72a7-72c7-bb4b-0c2c1499813d

Vinkius Inspector

Compliance Grade A+

Score 98.33/100

Report View Report ↗

Here's how it actually works

The bottom line is that your agent handles the full sequence: scope > setup > configure > deploy.

First, your agent lists all available teams (list_teams) to identify the correct scope for the new infrastructure. Then, it creates a specific project under that team using create_project, which links the service to its code base.

Next, you use list_environments and add_variable to provision separate testing areas (dev/staging) and inject all necessary secrets and configuration keys for those isolated instances. Finally, running deploy_environment pushes the latest code into place.

Who is this actually for?

This MCP targets the operations engineer who's tired of clicking through complex web dashboards at 2 am. It’s essential for platform teams and DevOps experts managing multiple, interconnected services. You need this if your release cycle involves more than two people manually checking status codes.

DevOps Engineer

Manages continuous deployment by triggering deployments (deploy_environment) or debugging failures using get_deployment_logs.

SRE (Site Reliability Engineer)

Audits the health of production services, checking metrics via get_project_analytics, and managing environment variables with add_variable.

Platform Architect

Designs and manages the overall infrastructure by listing all teams (list_teams) and creating new projects (create_project).

What Changes When You Connect

Automate the entire deployment pipeline. Instead of manually running a build, then deploying to dev, and finally repeating it for staging, use deploy_environment to trigger changes across all environments with one command.

Keep sensitive data isolated. You can manage every variable—API keys, database strings, feature flags—using add_variable, guaranteeing that development secrets never accidentally leak into the production build.

Diagnose failures instantly. When a service breaks in staging, don't waste time guessing. Use get_deployment_logs and get_project_analytics to pull definitive proof of failure or performance degradation.

Speed up local testing dramatically. Before committing code, use get_tunnel_ticket. This gives you a working URL for immediate testing on your machine without having to deploy anything first.

Maintain visibility across the board. Use list_projects at any time. It provides an instant, high-level overview of every service running in your account and its current operational status.

See it in action

01 01

The new payment gateway needs testing.

A developer can't test the latest payment integration because it only works on isolated branches. They use get_tunnel_ticket to generate a local URL, bypass staging completely, and confirm the entire flow before requesting an official deployment.

02 02

We need to roll back a service update.

The latest code broke production because of a configuration mismatch. An agent checks get_project for the last known good configuration and uses update_project or triggers a targeted rollback deployment using deploy_environment.

03 03

Audit all service credentials.

The security team needs to verify every secret. They run list_environments first, then use list_variables on the critical production environment to ensure no stale or unneeded keys remain.

04 04

Onboard a new microservice.

A platform team needs to track a brand-new service. They start by running create_project, linking it to git, and then immediately use get_server_info to confirm the project is alive and reporting correctly.

The honest tradeoffs

Manually passing variables.

Anti-pattern

A developer copies a database URL from their local config file into the staging environment's UI, risking typos or using outdated credentials. This is slow and error-prone.

The Fix

Forgetting to check logs after deployment.

Anti-pattern

The agent reports 'Success!' on deploy_environment, but nothing actually works because the service failed silently due to a missing variable. They waste hours debugging the wrong place.

The Fix

Updating config without checking existing variables.

Anti-pattern

An engineer manually deletes a critical environment variable, forgetting that another service relies on it. This breaks multiple systems simultaneously.

The Fix

Anti-pattern

The Fix

Use list_variables to audit current settings before deletion or update, then use add_variable for controlled updates. Always verify the outcome with get_deployment_logs after any change.

When It Fits, When It Doesn't

You should use this MCP if your core pain point is managing complexity across multiple services and environments. This isn't a tool for single-service deployments; it’s for infrastructure orchestration. Use this if you need to control the full lifecycle—from create_project through deployment, variable management, and final publishing (publish_to_registry).

Don't use this just because you want to 'monitor things.' If your only requirement is viewing a simple dashboard metric without changing anything, maybe another dedicated monitoring tool suffices. But if the job requires making changes—like spinning up create_environment, or pushing code via deploy_environment—then this MCP is necessary. It handles the state machine of deployment for you; it’s where your agent needs to live.

Questions you might have

How do I check if my service is running correctly using get_server_info? +

You use get_server_info to confirm operational status and list all currently exposed functions. This confirms the MCP is active before you try deploying or reading data.

What if I need to test my code locally before I run deploy_environment? +

Use get_tunnel_ticket first. It generates a temporary URL and token, letting your agent test the service on your machine without affecting any live environments.

Can I update an environment variable using add_variable if it already exists? +

Yes. add_variable is designed to manage existing configuration keys, allowing you to securely overwrite or set new values for specific project/environment combinations.

Which tool should I use to see all my running projects in one place? list_projects? +

You must run list_projects first. It gives a complete overview of every service and its associated deployment status across your entire account.

If I want to audit environment variables before deploying a change, should I use `list_variables`? +

Yes, running list_variables shows you every variable key assigned to an environment. You can check if critical secrets, like API keys or database URLs, are present and configured correctly for your project.

Before I run `create_project`, how do I find the correct team ID using `list_teams`? +

You must call list_teams first. This returns all associated teams, giving you the necessary IDs to ensure your new MCP project is placed under the right organizational umbrella.

If my deployment fails, should I check `get_deployment` or use `get_deployment_logs`? +

You need to use get_deployment_logs. While get_deployment tells you if the process succeeded or failed, the logs provide the actual build output and stack traces necessary for deep debugging.

How can I review performance trends and usage patterns using `get_project_analytics`? +

This tool provides usage metrics, including request counts and latency history. Reviewing these data points helps you spot performance bottlenecks or track if a specific function is generating unexpected error rates.

Can I deploy environments from specific Git branches directly from the terminal? +

Yes! You can operate create_environment mapping parameters matching your active branches, then instruct your agent to trigger the deploy_environment sync.

Are environment variables secured during deployment processes? +

Absolutely. You can invoke add_variable providing exact tokens without storing them in repositories. They remain encrypted at rest and dynamically injected upon startup.

Can I test server configuration before final production merges? +

Yes. Request your AI agent to trigger get_tunnel_ticket enabling you to natively tunnel local host environments through Alpic before pushing any true integration commits.

Connect to your AI in seconds.

Add variable

Create environment

Get tunnel ticket

Alpic: 18 Tools for Full MCP Management

Make your AI actually useful.

Add Variable

Create Environment

Get Tunnel Ticket

Create Project

Delete Project

Delete Variable

Deploy Environment

Get Deployment Logs

Get Deployment

Get Project Analytics

Get Project

Get Server Info

List Environments

List Projects

List Teams

List Variables

Publish To Registry

Update Project

Security and governance baked right in.

Claude AI

Open Claude Settings

Add Custom Connector

Start a conversation

Claude Code

Open your terminal

Add the MCP Server

Start coding

Cursor

One-Click Install (Recommended)

Open Cursor Settings

Add New Server

Use in Composer

Antigravity

Configure Agent Environment

Bind the Endpoint

Execute

VS Code Copilot

One-Click Install (Recommended)

Open MCP Settings

Add Server Config

Windsurf

One-Click Install (Recommended)

Open Windsurf Settings

Add Server Endpoint

LangChain

Install Dependencies

Connect the Server

CrewAI

Define the Tool

Execute Task

Choose How to Get Started

Build Your Own

Make Your AI Do More

Works with Claude, ChatGPT, Cursor, and more

The tedious, multi-tab dashboard dance.

Managing your service lifecycle with Alpic MCP

What your AI can actually do with this

Here's how it actually works

Who is this actually for?

What Changes When You Connect

See it in action

The new payment gateway needs testing.

We need to roll back a service update.

Audit all service credentials.

Onboard a new microservice.

The honest tradeoffs

Manually passing variables.

Forgetting to check logs after deployment.

Updating config without checking existing variables.

When It Fits, When It Doesn't

Questions you might have