My MCP Server
A template for deploying and monetizing MCP servers on Apify, supporting stdio and streamable HTTP transports with authentication and pay-per-event billing.
README
MCP server template
<!-- This is an Apify template readme -->
A template for running and monetizing a Model Context Protocol server using stdio transport on Apify platform. This allows you to run any stdio MCP server as a standby Actor and connect via either the streamable HTTP transport with an MCP client.
Note: the check_dependency_tree_risk tool only scans the first 100 packages found in a package-lock.json and only returns details for Medium-severity-or-above findings, to keep results bounded on very large dependency trees.
How to use
Change the MCP_COMMAND to spawn your stdio MCP server in src/main.ts, and don't forget to install the required MCP server in the package.json (using npm install ...).
By default, this template runs an Everything MCP Server using the following command:
const MCP_COMMAND = [
'npx',
'@modelcontextprotocol/server-everything',
];
Alternatively, you can use the mcp-remote tool to turn a remote MCP server into an Actor. For example, to connect to a remote server with authentication:
const MCP_COMMAND = [
'npx',
'mcp-remote',
'https://mcp.apify.com',
'--header',
'Authorization: Bearer TOKEN',
];
Feel free to configure billing logic in .actor/pay_per_event.json and src/billing.ts.
Push your Actor to the Apify platform, configure standby mode, and then connect to the Actor standby URL with your MCP client using the endpoint: https://me--my-mcp-server.apify.actor/mcp (streamable HTTP transport).
Important: When connecting to your deployed MCP server, you must pass your Apify API token in the Authorization header as a Bearer token. For example:
Authorization: Bearer <YOUR_APIFY_API_TOKEN>
This is required for authentication and to access your Actor endpoint.
Pay per event
This template uses the Pay Per Event (PPE) monetization model, which provides flexible pricing based on defined events.
To charge users, define events in JSON format and save them on the Apify platform. Here is an example schema with the tool-request event:
[
{
"tool-request": {
"eventTitle": "Price for completing a tool request",
"eventDescription": "Flat fee for completing a tool request.",
"eventPriceUsd": 0.05
}
}
]
In the Actor, trigger the event with:
await Actor.charge({ eventName: 'tool-request' });
This approach allows you to programmatically charge users directly from your Actor, covering the costs of execution and related services.
To set up the PPE model for this Actor:
- Configure Pay Per Event: establish the Pay Per Event pricing schema in the Actor's Monetization settings. First, set the Pricing model to
Pay per eventand add the schema. An example schema can be found in pay_per_event.json.
Resources
- What is Anthropic's Model Context Protocol?
- How to use MCP with Apify Actors
- Apify MCP server
- Apify MCP server documentation
- Apify MCP client
- Model Context Protocol documentation
- TypeScript tutorials in Academy
- Apify SDK documentation
Getting started
For complete information see this article. To run the Actor use the following command:
apify run
Deploy to Apify
Connect Git repository to Apify
If you've created a Git repository for the project, you can easily connect to Apify:
- Go to Actor creation page
- Click on Link Git Repository button
Push project on your local machine to Apify
You can also deploy the project on your local machine to Apify without the need for the Git repository.
-
Log in to Apify. You will need to provide your Apify API Token to complete this action.
apify login -
Deploy your Actor. This command will deploy and build the Actor on the Apify Platform. You can find your newly created Actor under Actors -> My Actors.
apify push
Documentation reference
To learn more about Apify and Actors, take a look at the following resources:
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.