What is the Model Context Protocol (MCP)?
MCP, MCP server
The Model Context Protocol (MCP) is an open standard, introduced by Anthropic in November 2024, for connecting AI assistants to external tools and data sources. An MCP server describes its tools in a common format, so assistants such as Claude, ChatGPT and Cursor can use them without a custom integration for each one.
MCP is an open standard for connecting AI assistants to tools and data.
One MCP server works with any assistant that supports the protocol.
Marketers use MCP to query analytics and run tasks from an AI assistant.
How the Model Context Protocol works
The Model Context Protocol works through a client and server model. The AI application, such as Claude or Cursor, acts as the host and runs an MCP client. Each external system exposes an MCP server that the client can connect to.
An MCP server can offer three main kinds of capability:
Tools: actions the model can call, such as fetching visibility data or creating a task.
Resources: data the model can read, such as files, records or reports.
Prompts: reusable templates for common workflows.
When a user asks a question, the large language model sees the available tools with their descriptions and input formats, decides which to call, and sends a request. The server runs the action and returns the result, which the model uses in its answer. Servers can run locally or remotely, with remote servers usually secured by OAuth sign-in.
MCP vs APIs
MCP differs from a traditional API in who it is designed for. A REST API is built for developers, who read documentation and write code for each integration. MCP is built for AI models, which read tool descriptions at run time and decide how to use them.
MCP usually sits on top of existing APIs rather than replacing them. An MCP server wraps a product's API and presents it in a standard format that any compatible assistant understands.
The main benefit is reuse. Before MCP, connecting five assistants to five tools could mean 25 custom integrations. With MCP, each tool ships one server and each assistant ships one client, and they work together. This shared layer is a building block for agentic search and other agent workflows, where AI systems act across many tools on a user's behalf.
How marketers use MCP
Marketers use MCP to work with their data and tools through an AI assistant instead of switching between dashboards. With the right MCP servers connected, a marketer can ask questions in plain language and have the assistant pull data, compare it and act on it.
Examples include:
asking an assistant to summarize this week's AI visibility changes and explain which competitors gained ground
pulling citation data and drafting a list of outreach targets in one conversation
creating or updating tasks in a project tool straight from an analysis
combining analytics from several tools into one report
This is part of the wider move toward agentic marketing, where AI agents do multi-step work across systems. MCP is a common way to give those agents scoped access to the tools they need.
Is MCP secure?
MCP security depends on how servers are built and which ones you connect. The protocol supports authentication such as OAuth for remote servers, and hosts typically ask users to approve tool calls. Risks still exist, mainly from connecting untrusted servers or giving a server more access than it needs.
Good practice for teams:
connect only MCP servers from vendors you trust
grant the narrowest permissions a workflow needs, such as read-only access for reporting
review tool calls that write or delete data before approving them
be careful with web content or shared documents that reach the model, since they can contain hidden instructions meant to misuse connected tools
Treat an MCP server like any other integration with access to company data, and review it the same way your team reviews agentic marketing tools.
How to use KIME through MCP
KIME MCP connects your KIME data to AI assistants that support the protocol, such as Claude, ChatGPT and Cursor. You can ask for visibility, share of voice, sentiment and citation data in plain language, manage tracked prompts, and create or update tasks in the Actions center without leaving your assistant. It is a practical way to bring AI search data into the tools your team already uses for analysis and reporting.
Frequently asked questions
Which AI assistants support MCP?
Support for the Model Context Protocol has spread across many AI tools. Claude was first, and ChatGPT, Cursor and a range of other assistants, code editors and agent frameworks now support MCP servers in some form. Support levels differ, for example in whether remote servers or write actions are allowed, so check each product's documentation.
Do I need to code to use an MCP server?
Usually not. Many MCP servers are hosted by the vendor and can be added to an assistant by entering a URL and signing in. Building your own MCP server does require development work, but using an existing one is closer to installing an integration: connect it, approve the permissions and start asking questions.
Who maintains the Model Context Protocol?
Anthropic introduced the Model Context Protocol in November 2024 as an open standard with a public specification and open-source SDKs. It has since been developed in the open with contributions from other companies and the developer community, which is part of why many AI vendors adopted it rather than building competing formats of their own.