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Jampp MCP Server: Real-Time AI-Driven Campaigns, Scalable ROI - MCP Implementation

Jampp MCP Server: Real-Time AI-Driven Campaigns, Scalable ROI

Jampp MCP Server: The ultimate real-time marketing optimization powerhouse, delivering unmatched ROI through scalable, AI-driven campaigns. Your success, amplified.

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About Jampp MCP Server

What is Jampp MCP Server: Real-Time AI-Driven Campaigns, Scalable ROI?

Jampp MCP Server is a middleware solution that bridges the Jampp advertising platform with external AI systems. It enables real-time data exchange between campaign performance metrics and machine learning models, empowering businesses to dynamically optimize ad spend and maximize returns. By exposing granular campaign data via the MCP protocol, this server allows AI-driven tools to perform automated bidding adjustments, audience targeting refinements, and performance forecasting in live environments.

How to Use Jampp MCP Server: Real-Time AI-Driven Campaigns, Scalable ROI?

Implementation Workflow

  1. Configure authentication credentials using OAuth 2.0 with automatic token refresh mechanisms
  2. Deploy the server instance to connect with your Jampp account through GraphQL endpoints
  3. Integrate with external AI systems via RESTful API interfaces for bidirectional data flow
  4. Set up real-time listeners for campaign performance updates and spend thresholds

Jampp MCP Server Features

Key Features of Jampp MCP Server: Real-Time AI-Driven Campaigns, Scalable ROI?

Adaptive Data Streaming

Provides continuous data feeds of impression, click, and conversion metrics at sub-minute granularity

Algorithmic Optimization Hooks

Includes predefined API methods for bid adjustments (±20% range) and budget reallocation

Multi-Tenant Security

Supports role-based access control for different advertising accounts and campaign groups

Performance Alerting

Triggers webhook notifications for predefined KPI deviations (CTR drops >15%, CPA spikes etc.)

Use Cases of Jampp MCP Server: Real-Time AI-Driven Campaigns, Scalable ROI?

  • Dynamic Bidding: Adjust bids in real-time based on competitor activity and inventory levels
  • Risk Mitigation: Automatically pause campaigns exceeding predefined cost thresholds
  • Multi-Channel Attribution: Correlate cross-channel performance data for accurate ROI calculations
  • Geotargeting Optimization: Dynamically adjust regional budgets using real-time location-based performance data
  • Anti-Fraud Measures: Block suspicious traffic patterns using machine learning anomaly detection

Jampp MCP Server FAQ

FAQ from Jampp MCP Server: Real-Time AI-Driven Campaigns, Scalable ROI?

How does the server ensure data accuracy?

Uses Jampp's verified reporting pipelines with built-in data validation checks and reconciliation features

What latency can be expected?

Typically 15-45 seconds between ad events and data availability in the API endpoints

Can I use this with custom ML models?

Yes - provides raw data feeds in JSON format with optional normalization parameters

Are there rate limits?

Includes adaptive rate limiting (500 requests/sec by default) with tiered API plans available

Content

Jampp MCP Server

This MCP server provides access to the Jampp Reporting API through the Model Context Protocol, allowing LLMs to fetch campaign performance data.

Features

  • OAuth 2.0 authentication with automatic token refresh
  • GraphQL-based API integration
  • Campaign spend reporting
  • Daily spend tracking
  • Comprehensive performance metrics
  • Asynchronous report generation and retrieval
  • Available metrics and dimensions listing

Prerequisites

  • Python 3.10 or higher
  • Jampp API credentials (Client ID and Client Secret)

Installation

  1. Clone this repository:

    git clone https://github.com/yourusername/mcp-jampp.git

cd mcp-jampp
  1. Install dependencies:

    uv add "mcp[cli]" httpx python-dotenv

Or with pip:

    pip install "mcp[cli]" httpx python-dotenv
  1. Set up your environment variables: Create a .env file in the project root with your Jampp API credentials:

    JAMPP_CLIENT_ID=your_client_id

JAMPP_CLIENT_SECRET=your_client_secret

Usage

Running the Server Directly

python jampp_mcp_server.py

Using with MCP Inspector

For development and testing:

mcp dev jampp_mcp_server.py

Integration with Claude Desktop

  1. Install Claude Desktop from claude.ai/download

  2. Configure Claude Desktop to use this server:

* Open or create `~/Library/Application Support/Claude/claude_desktop_config.json` (macOS) or `%APPDATA%\Claude\claude_desktop_config.json` (Windows)
* Add the following configuration:

    {
  "mcpServers": {
    "jampp": {
      "command": "python",
      "args": ["path/to/jampp_mcp_server.py"],
      "env": {
        "JAMPP_CLIENT_ID": "your_client_id",
        "JAMPP_CLIENT_SECRET": "your_client_secret"
      }
    }
  }
}
  1. Restart Claude Desktop

Available Tools

1. get_campaign_spend

Fetches campaign spend data for a specific date range.

Parameters:

  • from_date: Start date in YYYY-MM-DD format
  • to_date: End date in YYYY-MM-DD format
  • campaign_id (optional): ID of the specific campaign to query

2. get_campaign_daily_spend

Fetches daily spend data for a specific campaign.

Parameters:

  • from_date: Start date in YYYY-MM-DD format
  • to_date: End date in YYYY-MM-DD format
  • campaign_id: ID of the campaign to query
  • timezone (optional): Timezone for the report (default: UTC)

3. get_campaign_performance

Fetches comprehensive performance metrics for campaigns.

Parameters:

  • from_date: Start date in YYYY-MM-DD format
  • to_date: End date in YYYY-MM-DD format
  • campaign_id (optional): ID of a specific campaign to query
  • timezone (optional): Timezone for the report (default: UTC)

4. create_async_report

Creates an asynchronous report for larger data sets.

Parameters:

  • from_date: Start date in YYYY-MM-DD format
  • to_date: End date in YYYY-MM-DD format
  • dimensions: List of dimensions to include (e.g. ["campaignId", "campaign"])
  • metrics: List of metrics to include (e.g. ["impressions", "clicks", "spend"])

5. get_async_report_status

Checks the status of an asynchronous report.

Parameters:

  • report_id: ID of the async report to check

6. get_async_report_results

Gets the results of a completed asynchronous report.

Parameters:

  • report_id: ID of the async report to retrieve

7. get_available_metrics_and_dimensions

Gets a list of all available metrics and dimensions for reporting.

Example Queries for Claude

Here are some example queries you can ask Claude when using this MCP server:

  1. "Show me the campaign spend for the last 30 days"
  2. "What was the daily spend for campaign ID 12345 last week?"
  3. "Get me the performance metrics for all campaigns in January 2023"
  4. "Create an async report with campaign and country dimensions and impressions, clicks, and spend metrics for Q1"
  5. "What metrics and dimensions are available for Jampp reporting?"

License

MIT

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