From: aidotengineer
The Model Context Protocol (mCP) was developed by Anthropic’s applied AI team with the core concept that models are only as effective as the context provided to them [01:18:00]. Initially, context was manually provided through copy-pasting or typing into chatbots [01:33:00]. However, systems have evolved to allow models to directly access user data and context, making them more powerful and personalized [01:49:00]. mCP emerged as an open protocol to enable seamless integration of mCP with AI applications and agents with various tools and data sources [01:55:00], [02:00:00].
Standardization Efforts
mCP builds upon the principles of prior standardization protocols:
- APIs standardized web app interaction between front-end and back-end [02:16:00].
- LSP (Language Server Protocol) standardized how IDEs interact with language-specific tools, serving as a significant inspiration for mCP [02:37:00], [02:40:00].
mCP standardizes how AI applications interact with external systems through three primary interfaces [03:10:00], [03:17:00]:
- Prompts: User-controlled, predefined templates for common interactions with a server [01:03:00], [01:29:00]. These can be dynamic and interpolated with user or application context [02:01:00], [02:07:00].
- Tools: Model-controlled functions that the model within the client application can choose to invoke, such as reading or writing data to databases, CRMs like Salesforce, or local systems like Git [01:11:00], [01:21:00], [01:23:00], [01:05:00].
- Resources: Application-controlled data exposed to the application, such as images, text files, or JSON, providing a rich interface for applications and servers to interact beyond text [01:22:00], [01:27:00], [01:47:00]. Resources can also be dynamic and support notifications when updated [02:07:00], [02:11:00], [02:17:00].
Before mCP, there was significant fragmentation in how AI systems were built, with each team creating custom implementations for prompt logic, tool integration, and data access [03:47:00], [04:02:00]. mCP aims to standardize AI development and adoption of MCP, allowing any mCP client to connect to any mCP server with zero additional work [04:18:00], [04:46:00].
Value Proposition of Standardization
- Application Developers: Once an application is mCP compatible, it can connect to any server without extra development [05:42:00].
- Tool/API Providers: Build an mCP server once and see it adopted across various AI applications [05:51:00].
- End-Users: Access to more powerful and context-rich AI applications [06:28:00].
- Enterprises: Clear separation of concerns between teams, with one team owning the infrastructure (e.g., vector DB) as an mCP server, enabling other teams to build AI applications faster without needing direct access to the underlying data [06:48:00]. This mirrors the benefits seen with microservices [07:49:00].
Adoption and Future Outlook
The adoption of mCP has seen significant growth in recent months [08:05:00]:
- Applications and IDEs: Provides a way for users coding in IDEs to provide context to agents, which then interact with external systems like GitHub or documentation sites [08:21:00], [08:34:00].
- Server Side: Over 1100 community-built servers have been published open source [08:47:00]. Companies like Cloudflare and Stripe have published official integrations of mCP with AI applications [50:30].
- Open Source: Significant contributions to the core protocol and its infrastructure layer [09:07:00].
mCP is envisioned as a foundational protocol for agents [26:36:00]. It enables augmented LLM systems to query and write data to various systems, invoke tools, and maintain state, moving beyond a “fresh start” for every interaction [27:38:00], [28:10:00]. This allows agents to expand their capabilities even after initialization by discovering new interactions with the world [28:41:00].
Key Capabilities for Agent Adoption
- Sampling: Allows an mCP server to request LLM inference calls from the client, enabling the server to access intelligence without hosting its own LLM [53:49:00], [54:50:00]. The client retains control over privacy, cost, and model preferences [55:34:00], [55:51:00].
- Composability: Any application, API, or agent can act as both an mCP client and an mCP server, enabling complex, layered architectures where agents can chain interactions and specialize in particular tasks [56:19:00], [57:28:00].
Roadmap for Further Adoption
- Remote Servers and OAuth 2.0: Support for remotely hosted servers via SSE (Server-Sent Events) and OAuth 2.0 authentication will remove development friction and make servers more discoverable [01:13:28], [01:15:05].
- Official mCP Registry API: A unified, hosted metadata service (built in the open) will address fragmentation and installation issues of mCPs by providing a centralized way to discover, verify, and manage mCP servers [01:22:30], [01:23:09]. This will also support versioning of server capabilities [01:24:05]. This allows agents to be self-evolving, dynamically discovering new capabilities and data on the fly [01:36:01].
- Well-Known mCP Files: A standardized way for domains (e.g., shopify.com) to provide a
.well-known/mcp.json file, allowing agents to discover and utilize specific tools and resources from that domain, complementing the registry’s discoverability [01:39:27], [01:40:07]. This can coexist with computer use models, enabling agents to use APIs where available and UI interaction for the longtail [01:40:52]. - Medium-Term Considerations: The roadmap includes addressing stateful vs. stateless connections, streaming data, improved name spacing to prevent tool conflicts, and enhancing proactive server behavior (server-initiated actions and notifications) [01:41:31].
Overall, mCP aims to reduce complexity of creative tools with mCP by standardizing how AI applications and agents connect to external systems, fostering a robust ecosystem of reusable components and accelerating development and adoption of MCP [06:06:00].