# Marketing Intelligence MCP (`saifee_software/marketing-intelligence`) Actor

Stop guessing where your ad budget should go. Marketing Intelligence turns your sales and spend numbers into clear, ranked decisions on what to cut, keep, or scale next — no spreadsheets needed. It hands you a ready-to-run script to act on the answer immediately, right inside your AI assistant.

- **URL**: https://apify.com/saifee\_software/marketing-intelligence.md
- **Developed by:** [Saifee Software Solutions](https://apify.com/saifee_software) (community)
- **Stats:** 1 total users, 0 monthly users, 0.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

Pay per usage

This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-usage

## What's an Apify Actor?

An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.

Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md.

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For examples already wired to this Actor's own input schema, see the [API](#api) section below.

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).

# README

### 🚀 Python MCP Server Template

A Python template for deploying and monetizing a [Model Context Protocol (MCP)](https://modelcontextprotocol.io) server in the cloud using the [Apify platform](https://docs.apify.com/platform/actors).

This template enables you to:

- Deploy any Python stdio MCP server (e.g., [ArXiv MCP Server](https://github.com/blazickjp/arxiv-mcp-server)), or connect to an existing remote MCP server using Streamable HTTP or SSE transport
- Expose your MCP server via [Streamable HTTP](https://modelcontextprotocol.io/specification/2025-06-18/basic/transports#streamable-http) transport
- Monetize your server using Apify's Pay Per Event (PPE) model

### ✨ Features

- Support for **stdio-based, Streamable HTTP**, and SSE-based MCP servers
- **Built-in charging**: Integrated [Pay Per Event (PPE)](https://docs.apify.com/platform/actors/publishing/monetize#pay-per-event-pricing-model) for:
  - Server startup
  - Tool calls
  - Resource access
  - Prompt operations
  - List operations
- **Gateway**: Acts as a controlled entry point to MCP servers with charging and authorization logic
- **Session management**: Automatic session timeout and cleanup for idle connections (see [Session management challenges](#session-management-challenges) for details)

### Quick Start

1. Configure your MCP server in `src/main.py`:

   ```python
   # For stdio server:
   server_type = ServerType.STDIO
   MCP_SERVER_PARAMS = StdioServerParameters(
       command='your-command',
       args=['your', 'args'],
   )

   # For HTTP or SSE server:
   # server_type = ServerType.HTTP  # or ServerType.SSE
   # MCP_SERVER_PARAMS = RemoteServerParameters(
   #     url='your-server-url',
   # )
   ```

2. Add any required dependencies to the `requirements.txt` file (e.g. [arxiv-mcp-server](https://github.com/blazickjp/arxiv-mcp-server)).

3. Deploy to Apify and enable standby mode.

4. Connect using an MCP client:
   - Using [Streamable HTTP transport](https://modelcontextprotocol.io/specification/2025-06-18/basic/transports#streamable-http):
     ```json
     {
         "mcpServers": {
             "your-server": {
                 "url": "https://your-actor.apify.actor/mcp"
             }
         }
     }
     ```
   - Note: SSE endpoint serving has been deprecated, but SSE client connections are still supported.

### 💰 Pricing

This template uses the [Pay Per Event (PPE)](https://docs.apify.com/platform/actors/publishing/monetize#pay-per-event-pricing-model) monetization model, which provides flexible pricing based on defined events.

#### Charging strategy options

The template supports multiple charging approaches that you can customize based on your needs:

##### 1. Generic MCP charging

Charge for standard MCP operations with flat rates:

```json
{
    "tool-call": {
        "eventTitle": "MCP tool call",
        "eventDescription": "Fee for executing MCP tools",
        "eventPriceUsd": 0.05
    },
    "resource-read": {
        "eventTitle": "MCP resource access",
        "eventDescription": "Fee for accessing full content or resources",
        "eventPriceUsd": 0.0001
    },
    "prompt-get": {
        "eventTitle": "MCP prompt processing",
        "eventDescription": "Fee for processing AI prompts",
        "eventPriceUsd": 0.0001
    }
}
```

##### 2. Domain-specific charging (arXiv example)

Charge different amounts for different tools based on computational cost:

```json
{
    "search_papers": {
        "eventTitle": "arXiv paper search",
        "eventDescription": "Fee for searching papers on arXiv using the search_papers tool.",
        "eventPriceUsd": 0.001
    },
    "list_papers": {
        "eventTitle": "arXiv paper listing",
        "eventDescription": "Fee for listing available papers using the list_papers tool.",
        "eventPriceUsd": 0.001
    },
    "download_paper": {
        "eventTitle": "arXiv paper download",
        "eventDescription": "Fee for downloading a paper from arXiv using the download_paper tool.",
        "eventPriceUsd": 0.01
    },
    "read_paper": {
        "eventTitle": "arXiv paper reading",
        "eventDescription": "Fee for reading the full content of a paper using the read_paper tool.",
        "eventPriceUsd": 0.01
    }
}
```

##### 3. No charging (free service)

Comment out all charging lines in the code for a free service.

#### How to implement charging

1. **Define your events** in `.actor/pay_per_event.json` (see examples above). This file is not actually used at Apify platform but serves as a reference.

2. **Enable charging in code** by uncommenting the appropriate lines in `src/mcp_gateway.py`:

   ```python
   # For generic charging:
   await charge_mcp_operation(actor_charge_function, ChargeEvents.TOOL_CALL)

   # For domain-specific charging:
   if tool_name == 'search_papers':
       await charge_mcp_operation(actor_charge_function, ChargeEvents.SEARCH_PAPERS)
   ```

3. **Add custom events** to `src/const.py` if needed:

   ```python
   class ChargeEvents(str, Enum):
       # Your custom events
       CUSTOM_OPERATION = 'custom-operation'
   ```

4. **Set up PPE model** on Apify:
   - Go to your Actor's **Publication settings**
   - Set the **Pricing model** to `Pay per event`
   - Add your pricing schema from `pay_per_event.json`

#### Authorized tools

This template includes **tool authorization** - only tools listed in `src/const.py` can be executed:

**Note**: The `TOOL_WHITELIST` dictionary only applies to **tools** (executable functions).
Prompts (like `deep-paper-analysis`) are handled separately and don't need to be added to this list.

Tool whitelist for MCP server
Only tools listed here will be present to the user and allowed to execute.
Format of the dictionary: {tool\_name: (charge\_event\_name, default\_count)}
To add new authorized tools, add an entry with the tool name and its charging configuration.

```python
TOOL_WHITELIST = {
    ChargeEvents.SEARCH_PAPERS.value: (ChargeEvents.SEARCH_PAPERS.value, 1),
    ChargeEvents.LIST_PAPERS.value: (ChargeEvents.LIST_PAPERS.value, 1),
    ChargeEvents.DOWNLOAD_PAPER.value: (ChargeEvents.DOWNLOAD_PAPER.value, 1),
    ChargeEvents.READ_PAPER.value: (ChargeEvents.READ_PAPER.value, 1),
}
```

**To add new tools:**

1. Add charge event to `ChargeEvents` enum
2. Add tool entry to `TOOL_WHITELIST` dictionary with format: `tool_name: (event_name, count)`
3. Update pricing in `pay_per_event.json`
4. Update pricing at Apify platform

Unauthorized tools are blocked with clear error messages.

### 🔧 How it works

This template implements a MCP gateway that can connect to a stdio-based, Streamable HTTP, or SSE-based MCP server and expose it via [Streamable HTTP transport](https://modelcontextprotocol.io/specification/2025-06-18/basic/transports#streamable-http). Here's how it works:

#### Server types

1. **Stdio server** (`StdioServerParameters`):
   - Spawns a local process that implements the MCP protocol over stdio.
   - Configure using the `command` parameter to specify the executable and the `args` parameter for additional arguments.
   - Optionally, use the `env` parameter to pass environment variables to the process.

Example:

```python
server_type = ServerType.STDIO
MCP_SERVER_PARAMS = StdioServerParameters(
    command='uv',
    args=['run', 'arxiv-mcp-server'],
    env={'YOUR_ENV_VAR': os.getenv('YOUR-ENV-VAR')},  # Optional environment variables
)
```

2. **Remote server** (`RemoteServerParameters`):
   - Connects to a remote MCP server via HTTP or SSE transport.
   - Configure using the `url` parameter to specify the server's endpoint.
   - Set the appropriate `server_type` (ServerType.HTTP or ServerType.SSE).
   - Optionally, use the `headers` parameter to include custom headers (e.g., for authentication) and the `auth` parameter for additional authentication mechanisms.

Example:

```python
server_type = ServerType.HTTP
MCP_SERVER_PARAMS = RemoteServerParameters(
    url='https://mcp.apify.com',
    headers={'Authorization': 'Bearer YOUR-API-KEY'},  # Replace with your authentication token
)
```

Note: SSE transport is also supported by setting `server_type = ServerType.SSE`.

- **Tips**:
  - Ensure the remote server supports the transport type you're using and is accessible from the Actor's environment.
  - Use environment variables to securely store sensitive information like tokens or API keys.

##### Environment variables:

Environment variables can be securely stored and managed at the Actor level on the [Apify platform](https://docs.apify.com/platform/actors/development/programming-interface/environment-variables#custom-environment-variables). These variables are automatically injected into the Actor's runtime environment, allowing you to:

- Keep sensitive information like API keys secure.
- Simplify configuration by avoiding hardcoded values in your code.

#### Gateway implementation

The MCP gateway (`create_gateway` function) handles:

- Creating a Starlette web server with Streamable HTTP (`/mcp`) endpoint
- Managing connections to the underlying MCP server
- Forwarding requests and responses between clients and the MCP server
- Handling charging through the `actor_charge_function` (`Actor.charge` in Apify Actors)
- Tool authorization: Only allowing whitelisted tools to execute
- Access control: Blocking unauthorized tool calls with clear error messages

Key components:

- `create_gateway`: Creates an MCP server instance that acts as a gateway
- `charge_mcp_operation`: Handles charging for different MCP operations
- `TOOL_WHITELIST`: Dictionary mapping tool names to (event\_name, count) tuples for authorization and charging
- `ProxyServer`: Main server class with session management and timeout handling
- `SESSION_TIMEOUT_SECS`: Configurable timeout for idle session termination

#### MCP operations

The MCP gateway supports all standard MCP operations:

- `list_tools()`: List available tools
- `call_tool()`: Execute a tool with arguments
- `list_prompts()`: List available prompts
- `get_prompt()`: Get a specific prompt
- `list_resources()`: List available resources
- `read_resource()`: Read a specific resource

Each operation can be configured for charging in the PPE model.

### Session management challenges

MCP connections may not properly close when clients disconnect, keeping containers alive indefinitely.

- Containers remain running even after client disconnection
- Setting `stateless` mode and `json_response=true` doesn't resolve the issue
- Only manual session deletion via `DELETE /mcp` properly terminates connections

**Workaround**:
The template implements automatic session cleanup using internal DELETE requests after a configurable timeout (`SESSION_TIMEOUT_SECS = 300` seconds). This ensures containers are properly terminated when idle.

This is a temporary solution until the MCP specification or client implementations provide a more robust mechanism for session management.

### 📚 Resources

- [What is Anthropic's Model Context Protocol?](https://blog.apify.com/what-is-model-context-protocol/)
- [How to use MCP with Apify Actors](https://blog.apify.com/how-to-use-mcp/)
- [Apify MCP server](https://mcp.apify.com)
- [Apify MCP server documentation](https://docs.apify.com/platform/integrations/mcp)
- [Apify MCP client](https://apify.com/jiri.spilka/tester-mcp-client)
- [MCP Servers hosted at Apify](https://apify.com/store/categories/mcp-servers)
- [Model Context Protocol documentation](https://modelcontextprotocol.io)
- [Apify SDK documentation](https://docs.apify.com/sdk/js/)

### Getting started

For complete information [see this article](https://docs.apify.com/platform/actors/development#build-actor-at-apify-console). In short, you will:

1. Build the Actor
2. Run the Actor

### Pull the Actor for local development

If you would like to develop locally, you can pull the existing Actor from Apify console using Apify CLI:

1. Install `apify-cli`

   **Using Homebrew**

   ```bash
   brew install apify-cli
   ```

   **Using NPM**

   ```bash
   npm -g install apify-cli
   ```

2. Pull the Actor by its unique `<ActorId>`, which is one of the following:

   - unique name of the Actor to pull (e.g. "apify/hello-world")
   - or ID of the Actor to pull (e.g. "E2jjCZBezvAZnX8Rb")

   You can find both by clicking on the Actor title at the top of the page, which will open a modal containing both Actor unique name and Actor ID.

   This command will copy the Actor into the current directory on your local machine.

   ```bash
   apify pull <ActorId>
   ```

### Documentation reference

To learn more about Apify and Actors, take a look at the following resources:

- [Apify SDK for JavaScript documentation](https://docs.apify.com/sdk/js)
- [Apify SDK for Python documentation](https://docs.apify.com/sdk/python)
- [Apify Platform documentation](https://docs.apify.com/platform)
- [Join our developer community on Discord](https://discord.com/invite/jyEM2PRvMU)

# Actor input Schema

## Actor input object example

```json
{}
```

# Actor output Schema

## `results` (type: `string`):

No description

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {};

// Run the Actor and wait for it to finish
const run = await client.actor("saifee_software/marketing-intelligence").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {}

# Run the Actor and wait for it to finish
run = client.actor("saifee_software/marketing-intelligence").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{}' |
apify call saifee_software/marketing-intelligence --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,saifee_software/marketing-intelligence"
        }
    }
}
```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/MEUaiXebucIBcvn0b/builds/DHaX8j3tebXHiUt1I/openapi.json