LLM Chronicles #9: The Power Law of MCP
Understanding the basics of MCP
How does MCP work?
The Model Context Protocol (MCP) is a standardized framework that allows LLMs to communicate seamlessly with external tools or services. Here’s a brief overview of how it works:
Standardized Communication: MCP offers a universal protocol, ensuring LLMs can interact with various tools consistently without custom setups.
Tool Registry: Tools register in a system where LLMs can discover their capabilities, inputs, and outputs.
Discovery and Invocation: LLMs query the registry to find and dynamically use tools for specific tasks.
Context Management: MCP keeps track of the conversation or task context, enabling relevant tool use.
Security: It includes authentication and authorization for safe, controlled access.
This setup makes LLMs more versatile and efficient by letting them tap into external tools effortlessly.
A tool call in MCP
To discover available tools, clients send a tools/list request.
LLM chooses the tool using the tool info.
To invoke a tool, clients send a tools/call request.
What does MCP solve?
The M×N vs. M+N Problem
Imagine you have:
M different data sources (databases, documents, APIs, etc.)
N different consumers (AI models, applications, services)
Without a standard protocol:
You need M×N different integrations (each consumer needs a custom connection to each data source)
This creates an "exploding" problem - as you add new data sources or consumers, the number of required integrations grows multiplicatively
With a standard protocol like MCP:
You need M adaptors for your data sources to speak the protocol
You need N adaptors for your consumers to understand the protocol
Total integrations: M+N (which grows linearly, not multiplicative
The Power Law
The power law of networks, often called "network effects," means that the value of a network increases exponentially as more users join it. For protocols like MCP, this creates a self-reinforcing cycle.
The network consists of two main components that create value as they grow together:
MCP Servers (data providers): These are the systems that expose data through the MCP standard. This includes:
Pre-built servers for services like Google Drive, Slack, GitHub
Custom MCP servers built by organizations for their internal data
Open-source connectors contributed by the community
MCP Clients (AI tools): These are the AI applications that connect to MCP servers to access data, with Claude being a primary example.
The network growth happens through these mechanisms:
Two-sided network effects:
Each new MCP server adds value for all existing AI tools using MCP (they can now access more data sources)
Each new AI tool supporting MCP adds value for existing MCP servers (their data becomes accessible to more AI systems)
How the growth accelerates:
Initially, Anthropic creates core MCP servers and integrates with Claude Desktop
Early adopters implement MCP for their systems
As these implementations prove useful, more developers build custom MCP servers
Development tools (Zed, Replit, etc.) integrate MCP client capabilities
The growing ecosystem makes it increasingly attractive for new players to join
The open-source nature encourages contributions, further expanding available connectors
At some point, implementing MCP becomes the default choice rather than a special feature
This creates the exponential curve seen in the GitHub stars graph. As MCP usage grows, it becomes the de facto standard, and the barrier to entry for competing protocols increases dramatically because they lack the comprehensive ecosystem MCP has developed.
The document specifically mentions shifting from "fragmented integrations" to a "more sustainable architecture," which is the core value proposition driving the network growth - everyone benefits from standardization, but only if enough participants adopt the same standard.
Is this similar to a gateway?
MCP (Model Context Protocol) can be likened to a gateway in several ways, but it serves a more specific purpose in facilitating standardized communication between AI models and external systems. Here’s how MCP is similar to and different from a traditional gateway:
Similarities to a Gateway
Intermediary Role: Like a gateway, MCP acts as an intermediary between AI models and external tools or data sources. It provides a standardized interface for communication, allowing models to access and interact with diverse systems efficiently.
Standardization and Simplification: Just as a gateway simplifies interactions between different systems by providing a common interface, MCP standardizes how AI models connect to various tools and data sources, reducing the complexity associated with multiple custom integrations.
Real-Time Communication: Both gateways and MCP support real-time communication. In MCP, this is achieved through its client-server architecture, enabling AI models to dynamically retrieve information and trigger actions across different tools.
Differences from a Traditional Gateway
Dynamic Discovery and Adaptation: Unlike traditional gateways, which often rely on static configurations, MCP allows for dynamic discovery of available tools and services. This means AI models can adapt to changing environments without needing manual updates.
Two-Way Communication: MCP supports persistent, two-way communication, allowing AI models not only to retrieve data but also to trigger actions across connected systems. This is more advanced than typical gateways, which often focus on one-way data transfer.
Contextual Understanding: MCP is designed to handle contextual information effectively, ensuring that AI models can understand and respond appropriately based on the current state of the system. This is more sophisticated than traditional gateways, which typically do not manage context in such a nuanced way.
Can MCP win over Others?
MCP can win over others for several key reasons:
Backing by a Big Lab: MCP is supported by Anthropic, a well-established and respected entity in the AI field. This backing provides credibility and resources that smaller or less-known organizations like Composio might not have. The influence of a big lab can significantly impact adoption and trust in the standard1.
Detailed Specification: MCP has a comprehensive and well-documented specification, which is crucial for an open standard. This detailed spec helps developers understand and implement the protocol more easily, making it more attractive than standards with less thorough documentation1.
Ecosystem and Industry Adoption: MCP has gained significant traction with notable companies implementing it in production, such as Zed, Replit, and Sourcegraph. This widespread adoption creates a network effect that encourages more companies to join, further solidifying MCP's position2.
Flexibility and Compatibility: MCP allows for bridge servers, enabling other standards to integrate with it. This flexibility makes MCP more adaptable and appealing to a broader range of users and developers2.
AI-Native Design: MCP is designed with AI integrations in mind, providing features like dynamic tool discovery and real-time communication. This AI-native approach aligns well with the evolving needs of AI-driven workflows, making it more suitable for modern applications compared to more generic standard.
What is there for enterprises?
The MCP helps enterprises reduce both development and operational costs by providing a standardized, flexible, and secure framework for integrating LLMs with external tools and services. Below is a breakdown of how MCP achieves this:
1. Standardized Integration Cuts Development Time
What it does: MCP offers a universal protocol that standardizes how LLMs communicate with external tools (e.g., databases, APIs, or services).
Why it helps: Instead of building custom integrations for each tool—which requires learning new APIs, writing bespoke code, and maintaining it—MCP allows developers to use a single, consistent approach.
Cost savings:
Reduces development time by eliminating the need for tool-specific coding.
Lowers maintenance costs by minimizing errors and the need for ongoing updates.
2. Tool Registry Enables Flexibility
What it does: MCP includes a tool registry where external tools are registered and can be dynamically discovered by LLMs.
Why it helps: Enterprises can easily add, update, or remove tools without making significant changes to their systems.
Cost savings:
Avoids expensive system overhauls when business needs change.
Reduces reliance on specialized development resources, cutting adaptation costs.
3. Built-In Security Reduces Risk Costs
What it does: MCP includes authentication and authorization features to secure tool access and protect sensitive data.
Why it helps: It ensures compliance with regulations and reduces the risk of data breaches, which can be costly to address.
Cost savings:
Avoids expenses from fines or breach remediation.
Reduces the need for additional security investments.
How to create your MCP server in Python?
https://github.com/modelcontextprotocol/python-sdk
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