From: aidotengineer
The fundamental philosophy behind the Model Context Protocol (mCP) is rooted in the belief that models are only as effective as the context provided to them [00:01:18]. This core concept drives the development of mCP as an open protocol designed to enable seamless integration between AI applications and their tools and data sources [00:01:55].
Historical Precedent and Inspiration
mCP’s design draws inspiration from prior successful protocols that standardized interactions in other domains [00:02:10]:
- APIs: APIs emerged to standardize how web applications interact between the front-end and back-end, acting as a translation layer for requests [00:02:16]. This allowed front-ends to access servers, databases, and services [00:02:34].
- LSP (Language Server Protocol): LSP standardized how Integrated Development Environments (IDEs) interact with language-specific tools [00:02:40]. An LSP-compatible IDE can communicate with a single Go LSP server, granting access to all Go language features when coding [00:02:50].
mCP was born from this inspiration, aiming to standardize how AI applications interact with external systems [00:03:10].
Addressing Industry Fragmentation
Before mCP, the AI industry, and even individual companies, experienced significant fragmentation in building AI systems [00:03:41]. Teams would create custom implementations for AI applications to hook into context, often with unique prompt logic, diverse methods for integrating tools and data, and varying approaches to federating access [00:03:53]. This led to an “N times M problem” with numerous permutations for client-server interaction [00:06:06].
The World With mCP
mCP seeks to flatten this complexity by providing a standardized layer between application developers and tool/API developers [00:06:17]. In a world with mCP, AI development is standardized, allowing any mCP client (e.g., Perser, Windsurf, Goose) to connect to any mCP server with zero additional effort [00:04:15].
An mCP server acts as a wrapper, federating access to various systems and tools relevant to AI applications, such as databases, CRMs like Salesforce, or local systems like Git [00:04:52].
Value Proposition
The standardization offered by mCP provides value across the ecosystem:
- Application Developers: Once an application is mCP compatible, it can connect to any server without additional work [00:05:42].
- Tool/API Providers: Developers can build an mCP server once and see it adopted across various AI applications [00:05:51].
- End Users: Experience more powerful and context-rich AI applications that understand them and can take action in the real world [00:06:28].
- Enterprises: Enables a clear separation of concerns between different teams [00:06:48]. For example, a team managing a vector database can turn it into an mCP server, allowing other teams to build AI applications faster without needing to re-implement access logic [00:07:21]. This mirrors the benefits seen with microservices [00:07:49].
Core Concepts and Interfaces
mCP standardizes interactions through three primary interfaces: prompts, tools, and resources [00:03:17] [00:09:56]. A key philosophical distinction lies in their control:
- Tools: Model-controlled, meaning the LLM within the client application decides when to invoke them based on descriptions provided by the server [00:10:27]. Tools allow for reading and writing data, updating databases, or interacting with local file systems [00:11:04]. The choice to use a tool is often best when it’s ambiguous if or when it should be invoked [00:16:02].
- Resources: Application-controlled, exposing data (e.g., images, text files, JSON) from the server to the client application [00:11:23]. The application then decides how to use this data [00:11:45]. Resources offer a rich interface beyond simple text-based chatbot interactions [00:11:50] and can be static or dynamic, interpolated with context from the user or application [00:20:58].
- Prompts: User-controlled, serving as predefined templates for common interactions with a specific server [00:12:59]. This allows users to invoke specific functionalities, akin to “slash commands,” which then generate a longer, pre-defined prompt for the LLM [00:13:08].
This separation of control (model, application, user) is a deliberate design choice to give more control to each part of the system, not just the model [00:22:26].
mCP as a Foundational Protocol for Agents
A significant motivation and future vision for mCP is its role as the foundational protocol for agents [00:26:36].
Augmented LLMs and the Agentic Loop
The concept of an “augmented LLM” involves an LLM enhanced with access to retrieval systems, tools, and memory [00:27:29]. mCP functions as the underlying layer that federates and simplifies the LLM’s ability to communicate with these systems [00:28:10].
This means:
- Expandable Agents: Agents can expand their capabilities even after initial programming, discovering different interactions with the world [00:28:34].
- Focus on Core Logic: Agent builders can focus on the core agent loop, context management, and how the LLM processes data, rather than on the specifics of integrating diverse servers and tools [00:29:47].
- Open Capabilities: mCP provides these capabilities in an open way, allowing users to customize agents with their own context and preferred data interactions [00:29:21].
Key Protocol Capabilities for Agents
- Sampling: Allows an mCP server to request LLM inference calls (completions) from the client, rather than the server needing to host or interact with an LLM directly [00:53:52]. This federates requests and gives the client control over LLM hosting, model choices, privacy, and cost [00:54:52].
- Composability: An application, API, or agent can simultaneously be both an mCP client and an mCP server [00:56:34]. This enables chaining interactions and building complex, layered architectures where specialized LLM systems can interact [00:57:17]. Each node in the system can possess autonomy and make decisions on how to pull in richer data [01:01:17].
The combination of sampling and composability is particularly exciting for future agents, envisioning hierarchical systems of agents that can exist on the public web while maintaining privacy, security, and control [01:11:43].
Roadmap and Future Directions
The philosophy of mCP continues to drive its roadmap, focusing on features that reduce friction, enhance discoverability, and enable more sophisticated AI applications:
- Remote Servers and Authentication: The addition of OAuth 2.0 support allows for remotely hosted servers, removing the need for local setup and friction for users [01:13:59] [01:15:05]. This means mCP servers can exist like websites, discoverable and accessible without complex local configuration [01:15:47].
- mCP Registry API: A centralized, open, and hosted metadata service is being developed to address discoverability issues for mCP servers [01:22:27]. This registry will help users find trusted servers, understand their capabilities, and manage versioning [01:23:34]. Critically, it aims to make agents “self-evolving” by allowing them to dynamically discover and utilize new tools and data on the fly, without prior programming [01:35:59].
- Well-Known mCP: This concept provides a top-down approach for organizations to declare their mCP endpoint (e.g.,
well-known/mcp.json), allowing agents to discover and authenticate with official services [01:39:24]. This complements the registry by offering a verified way to access tools, especially when combined with computer use models (for long-tail interactions not covered by APIs) [01:40:52]. - Medium-Term Considerations: Future developments and roadmap for mCP also include exploring stateful vs. stateless connections, streaming data, improved namespacing for tools, and proactive server behavior (where servers can initiate interactions with clients) [01:41:31].
The overarching philosophy is to build an open protocol that fosters competition, benefits users and developers, and creates a highly connected and intelligent ecosystem for AI applications and agents [01:28:39].