One of the biggest challenges in prompting is ensuring the AI tool has access to all the context relevant to the task. Often, this context already exists within the systems we use all day: wikis, issue trackers, databases or observability systems. Seamless integration between AI tools and these information sources can significantly improve the quality of AI-generated outputs.
The Model Context Protocol (MCP), an open standard released by Anthropic, provides a standardized framework for integrating LLM applications with external data sources and tools. It defines MCP servers and clients, where servers access the data sources and clients integrate and use this data to enhance prompts. Many coding assistants have already implemented MCP integration, allowing them to act as MCP clients. MCP servers can be run in two ways: Locally, as a Python or Node process running on the user’s machine, or remotely, as a server that the MCP client connects to via SSE (though we haven't seen any usage of the remote server variant yet). Currently, MCP is primarily used in the first way, with developers cloning open-source MCP server implementations. While locally run servers offer a neat way to avoid third-party dependencies, they remain less accessible to nontechnical users and introduce challenges such as governance and update management. That said, it's easy to imagine how this standard could evolve into a more mature and user-friendly ecosystem in the future.
