MCP For Claude Marketplace
Alpic

Alpic MCP Connector for Claude

A+

AI MCP infrastructure: deploy, manage, and monitor MCP servers programmatically via agents.

18 tools Official Updated Jun 28, 2026 Official Vinkius Partner
More Details Connect to Claude

What you can do

Connect AI agents to the Alpic platform for complete MCP server lifecycle management:

  • List and manage teams with member access controls
  • Create, update, and delete MCP server projects with git repository linking
  • Deploy to multiple environments (dev, staging, production) with one command
  • Monitor deployments with real-time status, logs, and analytics
  • Manage environment variables securely for each deployment target
  • View analytics including request counts, latency, error rates, and usage patterns
  • Publish to the MCP registry to make your servers discoverable
  • Create development tunnels for local testing before production deployment

How it works

  1. Get your Alpic API token from team settings > API Keys
  2. Ask your AI agent to deploy servers, check logs, manage variables, or view analytics
  3. Natural language commands replace manual Alpic dashboard operations
  4. Multi-environment deployments with isolated configurations per environment

Who is this for?

Essential for MCP server developers, DevOps engineers, platform teams, AI infrastructure managers, and SaaS companies deploying MCP servers. Let AI agents handle continuous deployments, environment variable management, deployment debugging, and infrastructure monitoring. Perfect for teams managing 5+ MCP server environments who want to eliminate manual deployment steps, accelerate iteration cycles, and enable AI-driven infrastructure operations.

mcp-serverdeploymentlifecycle-managementinfrastructure-as-codemonitoringautomation

18 tools expose this connector's capabilities to your AI agent.

add_variable

Use this to set API keys, database URLs, feature flags, or any configuration needed by your MCP server. Requires project ID, environment ID, variable key, and value. Variable values are stored securely. Add a new environment variable to an Alpic environment

create_environment

Requires environment name and project ID. Optionally set initial variables and configuration. Each environment gets a unique URL for MCP client connections. Returns the created environment details. Create a new deployment environment (dev, staging, prod) for an Alpic project

get_tunnel_ticket

Returns the tunnel URL and ticket token. Use this during development to test your MCP server before deploying to a production environment. Get a tunnel ticket for local development and testing of an MCP server

create_project

Requires project name and team ID. Optionally set description, repository URL, and initial configuration. Returns the created project details including the new project ID needed for subsequent operations. Create a new MCP server project in Alpic

delete_project

This action cannot be undone. Use with caution. Requires the project ID. Confirm with the user before proceeding. Delete an Alpic MCP server project

delete_variable

Use this to clean up unused configuration keys. Requires project ID, environment ID, and variable key. Delete an environment variable from an Alpic environment

deploy_environment

The deployment runs asynchronously. Returns the deployment ID which can be used with get_deployment to check status. Use this to push new MCP server versions to dev, staging, or production environments. Trigger a new deployment for a specific Alpic environment

get_deployment_logs

Useful for debugging failed deployments, understanding build output, or verifying successful startup of the MCP server. Requires project ID and environment ID. Get deployment logs for a specific Alpic environment

get_deployment

Requires the deployment ID. Use this to check if a deployment succeeded, review deployment history, or debug failed deployments. Get detailed status and metadata for a specific Alpic deployment

get_project_analytics

Requires the project ID. Use this to monitor MCP server health, identify performance trends, and troubleshoot issues. Get analytics and usage data for a specific Alpic project

get_project

Requires the project ID from list_projects results. Use this to review project settings before making updates or triggering deployments. Get detailed information about a specific Alpic MCP server project

get_server_info

Use this to verify which MCP tools are exposed and confirm the server is running correctly. Get server information and status for a specific Alpic project

list_environments

Each environment has its own URL, variables, and deployment status. Returns environment IDs, names, URLs, and current deployment state. Use this to identify which environment to deploy to or manage variables for. List all environments (dev, staging, prod) for a specific Alpic project

list_projects

Returns project IDs, names, descriptions, associated teams, deployment status, and environment counts. Use this to overview your entire MCP infrastructure before managing specific projects or triggering deployments. List all MCP server projects in your Alpic account

list_teams

Each team contains projects and environments for deploying MCP servers. Returns team IDs, names, and member counts. Use this first to identify which team to manage projects under. List all teams associated with your Alpic account

list_variables

Variable values are masked for security. Returns variable keys and metadata. Use this to audit environment configuration before deploying or adding new variables. List all environment variables configured for an Alpic environment

publish_to_registry

Requires project ID and optionally a server description and category. Use this to make your MCP server publicly available. Publish an MCP server to the official MCP registry via Alpic

update_project

Only pass the fields you want to change. Requires the project ID from list_projects results. Use this to rename projects, update descriptions, or point to a new repository branch. Update an existing Alpic MCP server project configuration

See how to talk to your AI agent using Alpic.

List all active Alpic projects running on my account natively, then check the error rate metric for the first one listed.

Retrieving infrastructure topologies (`list_projects`). Found the 'CustomerSupport_AI' server project actively bound. Proceeding to fetch corresponding analytics (`get_project_analytics`)... Current metrics establish a < 1% error generation state mapping cleanly.

Deploy the staging environment for our main enterprise project mapped on isolated branches.

Starting systematic progression executing `deploy_environment`. The directive fired securely triggering asynchronous backend processing natively without interrupting visual processes.

Audit the credentials in our production environment. Provide exact details of variable schemas missing from active lists.

Auditing environment instances mapping arrays (`list_variables`). I have extracted the configured mapping elements excluding raw keys. The parameters indicate matching schemas with zero anomalies.

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

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