Understanding LLMs
Large Language Models (LLMs) have revolutionized AI interaction. These deep learning models, trained on vast text datasets, can generate human-like text, translate languages, answer questions, and write code.
LLMs use transformer architecture with self-attention mechanisms to process text through:
- Tokenization (breaking text into tokens)
- Embedding (converting tokens to numerical vectors)
- Processing (analyzing through multiple layers)
- Generation (predicting the next likely token)
LLMs are stateless, remembering only what’s in their context window, requiring carefully constructed prompts.
Limitations of Current Prompting Methods
Despite their capabilities, LLM interaction faces significant challenges:
- Vendor-Specific Implementations: Each provider (OpenAI, Anthropic, Google) has unique approaches to function calling and tool usage, forcing developers to maintain multiple codebases.
- Limited Context Management: No standardized way to track state, reference external resources, or exchange data types efficiently.
- Inefficient Resource Utilization: Developers often include excessive information “just in case,” leading to higher token usage and costs.
- Poor Separation of Concerns: Applications mix UI/UX logic, prompt engineering, tool implementation, and state management.
Why We Needed Something Better
Several factors drove the need for a better solution:
- Provider Proliferation: The explosion of LLM providers created a fragmented ecosystem.
- Application Complexity: As AI apps evolved beyond simple chatbots, ad-hoc approaches became unsustainable
- Enterprise Integration: Organizations required consistent security models and predictable behavior
- Rising Costs: Inefficient prompting led to excessive token consumption and higher operational costs
- Ecosystem Interoperability: Previous technology waves (web, mobile, cloud) succeeded through standardization
- Developer Experience: Teams needed familiar patterns and cleaner separation of concerns
Function Calling Challenges
Function calling allows LLMs to interact with external systems, but current implementations face several issues:
- Inconsistent Implementation: Each provider uses different parameter naming, function specifications, and response formats
- Limited Semantic Understanding: LLMs struggle with tool selection, parameter formatting, and multi-step planning
- Inefficient Context Usage: Function definitions consume valuable context space
- Security Concerns: Limited validation and overprivileged access
- State Management Problems: Each interaction starts from scratch, losing information from previous calls
Introducing MCP: A Standardized Protocol
Model Context Protocol (MCP) is a standardized protocol for LLM interactions, similar to how HTTP standardized web communications. It started as an Anthropic project to make it easier for AI models to interact with tools and data sources. However, it’s not just an Anthropic initiative anymore – MCP has evolved into an open standard with growing adoption among companies and developers across the industry It defines:
- How tools are described
- How requests and responses are structured
- How context is shared
- How rich data types are exchanged
MCP provides:
- Interoperability: Write once, deploy across providers
- Separation of concerns: Clean boundaries between components
- Efficient state management: Maintain context without redundant prompting
- Standardized tool descriptions: Consistent capability descriptions
- Rich data exchange: Support for complex data types
MCP Architecture
MCP follows a client-server architecture with five key components:
- MCP Hosts: Programs accessing data/functionality (LLM apps, IDEs)
- MCP Clients: Protocol implementations maintaining connections with servers
- MCP Servers: Lightweight programs exposing specific capabilities
- Local Data Sources: Resources on the user’s computer
- Remote Services: External systems available over the internet
Communication occurs through standardized protocol layers handling message framing, data exchange, and message formats.
Powerful Use Cases
MCP enables:
- Multi-Provider Applications: Switch seamlessly between providers
- Tool Marketplaces: Create portable tools across compatible hosts
- Stateful Agents: Maintain consistent state across interactions
- Rich Media Processing: Handle various media types consistently
- Enterprise Integration: Connect LLMs to internal systems with consistent interfaces
Traditional vs. MCP Approach
| Traditional Approach | MCP Approach |
|---|---|
| Provider-specific formatting | Standardized tool descriptions |
| Tightly coupled tool descriptions | Clean separation of concerns |
| Mixed execution logic and prompt management | Independent state management |
| Manual state tracking | Efficient rich data exchange |
| Major code changes to switch providers | Minimal changes to switch providers |
Getting Started
To build with MCP:
- Choose an MCP client library in your preferred language
- Create an MCP server implementing your desired tools
- Connect your LLM application using the MCP protocol
Conclusion
MCP isn’t revolutionary—it’s evolutionary. It standardizes existing patterns to make LLM applications more maintainable, portable, and scalable. By providing a common language for LLMs and tools, MCP creates the foundation for an ecosystem of interoperable components, similar to what HTTP did for the web.
As the LLM ecosystem matures, MCP will become increasingly important for powering the next generation of AI applications.
