Kickbacks MCP Server

Kickbacks MCP Server

MCP server for Kickbacks.ai that pays developers ad revenue from AI coding agent spinners. Provides tools to check balance, earnings, status, ad history, and toggle ads.

Category
Visit Server

README

Kickbacks.ai MCP Server

PyPI Python License

Earn ad revenue from AI coding agent spinners — MCP server for Kickbacks.ai that exposes tools to check balance, earnings, status, ad history, and enable/disable ads.

What is Kickbacks.ai?

Kickbacks.ai pays developers 50% of ad revenue from ads shown in AI coding agent spinners (Claude Code, Codex). While your agent "thinks", its spinner shows sponsored lines instead of generic verbs like "Discombobulating...".

Platform Status
VS Code Extension (Claude Code) ✅ Primary
VS Code Extension (Codex) ⚠️ Temp disabled
Terminal CLI (Claude Code 2.1.143+) ✅ Works

Quick Start

1. Get API Credentials

Email support@kickbacks.ai for partner API access.

2. Install & Configure

# Install via uvx (recommended)
uvx kickbacks-mcp

# Or install locally
pip install kickbacks-mcp
# or
pip install -e .

3. Set Environment Variables

export KICKBACKS_API_KEY=your_api_key_here
export KICKBACKS_USER_ID=your_user_id_here  # optional

4. Add to Hermes Agent Config

# ~/.hermes/config.yaml
mcp_servers:
  kickbacks:
    command: "uvx"
    args: ["kickbacks-mcp"]
    env:
      KICKBACKS_API_KEY: "your_key"

5. Use in Any MCP-Compatible Agent

# Check balance
> kickbacks_balance

# See earnings breakdown
> kickbacks_earnings

# Check status & caps
> kickbacks_status

# View ad history
> kickbacks_ads_history

# Enable/disable ads
> kickbacks_set_enabled enabled=true

Available Tools

Tool Description
kickbacks_balance Current balance (total earnings)
kickbacks_earnings Breakdown: today/week/month/total
kickbacks_status Connection state, caps, session stats
kickbacks_ads_history Impression/click log with pagination
kickbacks_set_enabled Enable/disable ads
kickbacks_config Check configuration status

Example Outputs

> kickbacks_earnings
Earnings Breakdown:
  Today: $0.42
  This Week: $7.11
  This Month: $42.50
  Total: $156.78
  Currency: USD

> kickbacks_status
Connected: ✅ | Authenticated: ✅ | Enabled: ✅ | Last sync: 2026-06-13T21:32:31 | Session: 23 impressions, 2 clicks

> kickbacks_ads_history
Ad History (showing 2 of 2):
  👁 [2026-06-13T21:32:31] Ramp - save time and money (spinner) - $0.0010
  👆 [2026-06-13T21:32:31] Bitcoin Devs Takeover Toronto (statusbar) - $0.0500

Configuration

Environment Variables

Variable Required Description
KICKBACKS_API_KEY Yes API key from Kickbacks.ai
KICKBACKS_USER_ID No Your Kickbacks user ID
KICKBACKS_API_BASE No Custom API base URL (default: https://api.kickbacks.ai/v1)

Hermes Agent Config

mcp_servers:
  kickbacks:
    command: "uvx"
    args: ["kickbacks-mcp"]
    env:
      KICKBACKS_API_KEY: "ghp_xxx..."
      KICKBACKS_USER_ID: "user_123"
    timeout: 60

Claude Desktop Config

{
  "mcpServers": {
    "kickbacks": {
      "command": "uvx",
      "args": ["kickbacks-mcp"],
      "env": {
        "KICKBACKS_API_KEY": "your_key"
      }
    }
  }
}

Development

Local Development

# Clone
git clone https://github.com/msgok/kickbacks-mcp
cd kickbacks-mcp

# Install in dev mode
pip install -e ".[dev]"

# Run directly
python -m kickbacks_mcp.server

# Run tests
pytest

Project Structure

kickbacks_mcp/
├── __init__.py      # Package init
├── client.py        # Kickbacks API client
├── server.py        # MCP server with tools
├── pyproject.toml   # Project config
└── README.md        # This file

Adding Real API Support

When Kickbacks provides API access:

  1. Edit client.py → replace _mock_response() with real HTTP calls
  2. Update data models if API differs
  3. Test with pytest tests/

Publishing to PyPI

# Build
pip install build
python -m build

# Publish
pip install twine
twine upload dist/*

License

MIT License - see LICENSE for details.

Support

  • Kickbacks API Access: support@kickbacks.ai
  • Issues: GitHub Issues
  • Kickbacks Website: https://kickbacks.ai

Built for the Hermes Agent ecosystem. Works with any MCP-compatible agent.

Recommended Servers

playwright-mcp

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.

Official
Featured
TypeScript
Magic Component Platform (MCP)

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.

Official
Featured
Local
TypeScript
Audiense Insights MCP Server

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.

Official
Featured
Local
TypeScript
VeyraX MCP

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

Official
Featured
Local
graphlit-mcp-server

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.

Official
Featured
TypeScript
Kagi MCP Server

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.

Official
Featured
Python
E2B

E2B

Using MCP to run code via e2b.

Official
Featured
Neon Database

Neon Database

MCP server for interacting with Neon Management API and databases

Official
Featured
Exa Search

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.

Official
Featured
Qdrant Server

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Official
Featured