Kochava MCP (Model Context Protocol) | Definition
Kochava MCP refers to Kochava’s implementation of Model Context Protocol (MCP)—an open standard enabling AI systems to securely and contextually interact with external tools and data hubs via APIs. Integrated within StationOne, Kochava’s integrative AI productivity platform, Kochava MCPs empower marketers to engage directly with attribution data, campaign insights, and other marketing intelligence within their Kochava accounts for:
- Mobile measurement partner (MMP) services
- Marketing mix modeling (MMM)
- Search ads automation
- Publisher and platform tools
- Other product solutions
Rather than leaving users to connect standalone MCPs—which can yield inconsistent or unreliable outputs—Kochava curates the MCP experience through StationOne’s orchestration layer, ensuring that each interaction is wrapped in the appropriate domain context, privacy safeguards, and prompt engineering. Kochava MCPs are reliable building blocks for agentic AI use cases across digital marketing, measurement, and beyond.
How Kochava MCPs Work
- Integration layer: StationOne acts as the orchestration environment where Kochava MCPs interface with Kochava products and third-party tools including Slack, Salesforce, or Gmail.
- Purpose: MCPs bridge large language models (LLMs) with real-world marketing systems such as attribution APIs, analytics sources, and campaign data repositories.
- Contextual intelligence: Each MCP execution is enriched with domain-specific context and validation logic to ensure that results are accurate, actionable, and less prone to model hallucinations.
- Privacy and security: StationOne runs locally, preserving data privacy and enterprise control—critical for regulated and data-sensitive industries.
- Agentic AI enablement: Through StationOne’s Agent Forge, marketers can create personalized AI agents powered by Kochava MCPs to automate workflows, generate insights, and report key performance metrics.
Why Kochava MCPs Matter for Marketers
Kochava MCPs extend beyond traditional AI integrations by solving the context orchestration problem that has limited generic MCP implementations. It allows marketing teams to
- Ask conversational questions about campaign data and receive real-time, structured answers
- Automate routine measurement and reporting workflows via AI agents
- Combine Kochava data with external productivity platforms in a secure, model-agnostic environment
- Scale AI-assisted marketing operations without sacrificing contextual precision or compliance
As part of the broader Kochava measurement ecosystem, Kochava MCPs represent a step toward integrative and agentic AI, where AI functions as a collaborative extension of human expertise rather than a detached automation layer.
Request an invite to the StationOne beta by visiting StationOne.ai/request-invite/.