Why we built—and donated—the Model Context Protocol (MCP)

Model Context Protocol (MCP) – A Deep Dive into its Development, Donation to the Linux Foundation, and Future

Key Concepts:

• Model Context Protocol (MCP): An open-source standard designed to connect Large Language Models (LLMs) to software and hardware, enabling real-world applications.
• Tool Calling: The ability of LLMs to utilize external tools and APIs to perform tasks beyond text generation.
• Agentic AI: AI systems capable of autonomous action and interaction with their environment.
• Statefulness: A characteristic of MCP where connections between servers and clients maintain ongoing sessions, unlike stateless API calls.
• Context Bloat: The issue of LLM context windows becoming overloaded with tool calls and intermediate data, reducing space for primary tasks.
• Innovator’s Dilemma: The challenge of balancing continued innovation with maintaining a stable and widely adopted standard.
• Open Source Community: The collaborative development model driving MCP’s evolution and adoption.

1. Origins and Problem Statement

The Model Context Protocol (MCP) originated from Anthropic’s internal need to connect LLMs, specifically Claude, to various developer tools like IDEs (Visual Studio Code, Zed) and cloud desktops. Previously, interaction with LLMs was cumbersome, requiring manual copy-pasting of data. The core problem MCP addresses is liberating LLMs from being “trapped in a box” and enabling them to interact with the tools and workflows users rely on daily. This initial effort, initially called “Cloud Connect” and then “Context Server Protocol (CSP),” quickly evolved into a protocol allowing any application to connect to any integration, avoiding the need to rewrite integrations for each LLM provider.

2. MCP as a Standard – The USB-C Analogy

MCP is designed as a standard, similar to USB-C, facilitating communication between applications and integrations using a common language. The goal is to avoid the proliferation of proprietary connectors and the pain of managing numerous incompatible connections, reminiscent of the connectivity challenges in the 1990s.

3. Development and Early Adoption (2023-2024)

The project began in late August 2023 with David and Justin Sparr at Anthropic. An internal hackathon in October 2023 demonstrated strong internal demand, with participants building MCP servers connecting to diverse applications, including 3D printers. This positive response led to the decision to open-source the project in November/December 2023. Initial traction was significant, with MCP gaining attention on Hacker News and attracting early adopters like Cursor, Block, Sourcegraph, and Kodium (Winer).

4. Key Features and Technical Considerations

• Protocol-Centric Design: MCP is designed as a protocol, enabling connections between any application and any integration, rather than being tied to a specific LLM provider.
• Stateful Connections: MCP utilizes stateful connections, maintaining sessions between servers and clients, which is crucial for complex, ongoing interactions. However, this introduces scaling challenges.
• Tool Calling & Context Bloat: MCP facilitates tool calling, allowing LLMs to leverage external tools. A current challenge is “context bloat,” where excessive tool calls and intermediate data consume valuable space in the LLM’s context window. Solutions being explored include tool search (loading only necessary tools) and programmatic tool calling (executing tools without storing intermediate results in the context).
• Tasks: A new feature enabling long-running operations and agent-to-agent communication.

5. Addressing Criticisms and Challenges

• Security: MCP opens potential security vulnerabilities due to the ability to ingest tools from unknown sources. Concerns include prompt injection and data exfiltration. Mitigation strategies involve model provider security measures and community-driven security audits.
• Context Bloat: As described above, this is being addressed through tool search and programmatic tool calling.
• Potential for Stifled Innovation: While standards can sometimes limit innovation, the open-source nature of MCP and the lack of mandated adoption are intended to foster continued development.
• Scaling Statefulness: Maintaining stateful connections at scale presents technical challenges that are currently being addressed.

6. Donation to the Linux Foundation & Agentic AI Foundation

To ensure the long-term stability and neutrality of MCP, Anthropic has donated the trademarks, code, and licensing rights to the Linux Foundation. This prevents any single entity from controlling the standard and guarantees its continued availability. A new foundation, the Agentic AI Foundation, has been created under the Linux Foundation umbrella, with founding members including Anthropic, Google, Microsoft, Amazon, Bloomberg, and Cloudflare. This foundation will focus on fostering open-source agentic AI projects, including MCP.

7. The Role of the Open Source Community

The success of MCP is heavily reliant on its open-source community. The community contributes to development, identifies vulnerabilities, and builds integrations. The open-source model allows for contributions from experts in areas where Anthropic lacks specialized knowledge, such as authentication standards. The analogy to open science and preprint servers (like arXiv) highlights the benefits of open collaboration and rapid dissemination of knowledge.

8. Future Directions

• Community Growth: Expanding the community of developers and contributors.
• Scaling & Statefulness: Addressing the technical challenges of scaling stateful connections.
• Enhanced UI Integration (MCP Apps): Developing richer user interfaces for interacting with LLMs through MCP, enabling more complex tasks like seat selection for flight bookings.
• Continued Innovation: Balancing the need for a stable standard with the desire for ongoing innovation.

9. Advice for Developers and Users

• For Developers: Build clients, build servers, integrate MCP into products, and actively participate in the community. Focus on creating seamless user experiences where the underlying technology (MCP) is transparent.
• For Users: Ideally, users shouldn’t need to know about MCP. The goal is for LLMs to seamlessly integrate with their workflows without requiring technical expertise.

10. Synthesis & Main Takeaways

The Model Context Protocol represents a significant step towards unlocking the full potential of LLMs by enabling them to interact with the real world. Its open-source nature, coupled with the donation to the Linux Foundation, ensures its long-term stability and fosters a collaborative ecosystem. While challenges remain, particularly around security, scalability, and context management, the active community and ongoing development efforts position MCP as a crucial component of the future of agentic AI. The project’s success underscores the power of open collaboration and the importance of establishing open standards to drive innovation and prevent vendor lock-in.