MCP Origins and Goals - MCP was created to address the challenge of constantly copying and pasting context into LLMs, aiming to give models the ability to interact with the outside world [4][5][6] - The goal is to establish an open-source, standardized protocol for model agency, enabling broader participation in the ecosystem [7][8] - Anthropic believes that enabling model agency is crucial for LLMs to reach the next level of usefulness and intelligence [8] MCP Development and Adoption - MCP was initially developed internally and gained traction during a company hack week [9][10] - Early feedback questioned the need for a new protocol and its open-source nature, given existing tool-calling capabilities [12][13] - Adoption by coding tools like Cursor marked a turning point, followed by broader adoption from Google, Microsoft, and OpenAI [14] Protocol Principles and Updates - The protocol prioritizes server simplicity, even if it increases client complexity, based on the belief that there will be more servers than clients [20][21] - Recent updates include support for streamable HTTP to enable more birectionality for agent communication [19] - Future development focuses on enhancing the agent experience, including elicitation to allow servers to request more information from end users [26][27] - Plans include a registry API to facilitate models finding MCPs independently, further supporting model agency [28] Ecosystem Opportunities - The industry needs more high-quality servers across various verticals beyond dev tools, such as sales, finance, legal, and education [31][34] - There is a significant opportunity in simplifying server building through tooling for hosting, testing, evaluation, and deployment [36] - Automated MCP server generation is a potential future direction, leveraging increasing model intelligence [37] - Tooling around AI security, observability, and auditing is crucial as applications gain more access to external data [38]