"Apache Spark" matching MCP servers:

Enables modeling and synthesis of creator paid amplification ROI, spark ads performance, and whitelisted return on ad spend (ROAS) for influencer marketing campaigns. It provides deterministic, zero-dependency calculations through MCP for use by AI agents and automation workflows.

A standalone MCP server that securely exposes Google Analytics data and Firebase Cloud Messaging capabilities to Spark/Gemini via SSE.

Open-source framework for AI agents (Claude Code, Cursor, Codex, Gemini) to autonomously operate Google Ads, Meta Ads, and Search Console. Grounded in a local STRATEGY.md — not metric-chasing. Defense-in-depth security, local-first. Apache 2.0.

Provides comprehensive SEO analysis by crawling websites and generating AI-powered keyword insights, search volume data, and competitor strategies. It delivers detailed reports on keyword clusters and commercial intent to help optimize digital marketing workflows.

Enables monitoring abandoned checkout sessions, diagnosing friction causes, crafting margin-preserving incentive vouchers, and generating multi-channel win-back messages through MCP.

Enables agents to run second-price Vickrey auctions that match consumer query intent against brand bids while keeping organic recommendations neutral. It also provides privacy-preserving ad monetization routing as a zero-dependency MCP server or importable Python module.

Enables analysis of cart items and shopper intent, computation of complementary product affinity scores, enforcement of merchant margin constraints, and synthesis of personalized discount bundles and cross-sell recommendations via MCP.

Enables multi-provider waterfall data enrichment for B2B prospects, firmographics, and verified decision-maker corporate emails via MCP.

Enables scanning e-commerce advertising content to detect prohibited claims and trademark violations, returning deterministic compliance evaluations for autonomous agents.

Enables scoring of customer leads and evaluation of interaction velocity to predict conversion outcomes. It exposes deterministic, dependency-free analysis through the Model Context Protocol for integration with MCP-compliant clients such as Claude Desktop and Cursor.

Enables AI agents and developers to generate Schema.org Product and FAQPage JSON-LD rich snippets from a query payload, returning structured output with execution telemetry. It runs as a zero-dependency Python MCP server that plugs into Claude Desktop, Cursor, and other MCP-compliant clients.

Enables MCP clients to score the retention quality of the first three seconds of shortform video hooks using audio/video telemetry and transcript input, returning deterministic JSON results for viral hook optimization.