Becoming an Expert in Anthropic’s Model Context Protocol (MCP)

By German Tirado, April 25, 2025

Anthropic’s Model Context Protocol (MCP) is an open standard that enables AI models (like large language models) to securely connect with external data sources and tools in a consistent way (Introducing the Model Context Protocol \ Anthropic) (Introducing the Model Context Protocol \ Anthropic). In other words, MCP acts as a “universal adapter” for AI, much like a USB-C port, standardizing how applications provide context and access to external resources (Introduction - Model Context Protocol) (Anthropic’s Model Context Protocol (MCP): A Deep Dive for Developers | by Amanatullah | Mar, 2025 | Medium). By open-sourcing MCP in late 2024, Anthropic aimed to solve the integration “M×N problem” – replacing countless one-off integrations between M models and N tools with a single protocol that any model or tool can implement (Anthropic Publishes Model Context Protocol Specification for LLM App Integration - InfoQ) (Model Context Protocol | Hacker News). This guide compiles the most up-to-date resources (2024–2025) to master MCP, from official documentation and specs to community discussions, tutorials, and comparisons with similar protocols.

Official Documentation and Specification

Official MCP Documentation Site (Anthropic) – The primary resource is the official MCP site which provides a User Guide, tutorials, and conceptual overviews. It introduces MCP’s purpose and architecture (client–server model) in simple terms: “MCP provides a standardized way to connect AI models to different data sources and tools,” analogous to how USB-C standardizes device connectivity (Introduction - Model Context Protocol). It explains why MCP (to integrate LLMs with external data for more relevant results (Introducing the Model Context Protocol \ Anthropic)) and the general architecture (an MCP host like Claude Desktop or an IDE uses an MCP client to talk to one or more MCP servers, each server exposing some data/tool via the protocol) (Introduction - Model Context Protocol) (A2A and MCP: Start of the AI Agent Protocol Wars? - Koyeb). Key concepts like Resources (data exposed to the model), Tools (actions the model can invoke), and Prompts (pre-defined prompt templates) are defined, along with guides on transports (STDIO, WebSocket, etc.) and security best practices.
MCP Technical Specification – Anthropic provides a formal specification (with version history) that details the protocol’s schema and requirements (Specification - Model Context Protocol) (Specification - Model Context Protocol). The spec defines how MCP uses JSON-RPC 2.0 messages for communication (Specification - Model Context Protocol), and enumerates the core primitives of the protocol: on the server side, Prompts (pre-set instructions/templates), Resources (structured data to include in model context), and Tools (executable functions the model can call); on the client side, Roots (entry points allowing servers to access client-side files) and Sampling (letting servers request model completions, with human oversight) (Anthropic Publishes Model Context Protocol Specification for LLM App Integration - InfoQ) (Anthropic Publishes Model Context Protocol Specification for LLM App Integration - InfoQ). This spec is essential for understanding MCP’s design at a low level and for implementing custom clients or servers compliant with the standard.
Anthropic’s Announcement (Nov 2024) – “Introducing the Model Context Protocol” is the official blog post by Anthropic announcing MCP (Introducing the Model Context Protocol \ Anthropic). It highlights the motivation: LLMs were “trapped behind information silos” and needed a universal way to access up-to-date data (Introducing the Model Context Protocol \ Anthropic). MCP is introduced as an open-source project to enable secure, two-way connections between data sources and AI-powered tools (Introducing the Model Context Protocol \ Anthropic). The announcement also unveiled the initial ecosystem: the MCP specification and SDKs, integration of MCP into the Claude desktop apps, and an open-source repository of MCP servers (Introducing the Model Context Protocol \ Anthropic). It mentions early adopters (Block, Apollo, etc.) and collaborators (Zed, Replit, Codeium, Sourcegraph) that began using MCP to enhance their AI systems (Introducing the Model Context Protocol \ Anthropic) (Introducing the Model Context Protocol \ Anthropic), underscoring that MCP was conceived as a community-driven standard.

SDKs, Repositories, and Sample Implementations

Official MCP SDKs – MCP has multi-language SDKs maintained in the open-source modelcontextprotocol GitHub organization. At the time of writing, official SDKs exist for Python, TypeScript, Java, Kotlin, and C# (Introduction - Model Context Protocol) (What's New - Model Context Protocol). These SDKs make it easier to implement MCP clients or servers in your language of choice. For example, the Python and TypeScript SDKs were released with MCP’s launch (Anthropic Publishes Model Context Protocol Specification for LLM App Integration - InfoQ), and in early 2025 Microsoft partnered with Anthropic to contribute an official C# SDK (available on NuGet) (Microsoft partners with Anthropic to create official C# SDK for Model Context Protocol - Microsoft for Developers) (Microsoft partners with Anthropic to create official C# SDK for Model Context Protocol - Microsoft for Developers). The SDK docs typically include usage examples, and the GitHub repos are open for contributions. Using an SDK is the fastest way to start building MCP integrations without implementing the JSON-RPC protocol from scratch.
Reference MCP Servers Repository – Anthropic provides a growing open-source repository of reference MCP servers (Introducing the Model Context Protocol \ Anthropic) (initially hosted by TrelisResearch on GitHub). This repo contains example implementations demonstrating how to expose various systems to LLMs via MCP (GitHub - TrelisResearch/mcp: Model Context Protocol Servers) (GitHub - TrelisResearch/mcp: Model Context Protocol Servers). For instance, it includes servers for file systems (secure file access), Git and GitHub (code repository interactions), Google Drive, Slack, PostgreSQL (read-only DB queries), Puppeteer (web browser automation), and more (GitHub - TrelisResearch/mcp: Model Context Protocol Servers) (GitHub - TrelisResearch/mcp: Model Context Protocol Servers). Each server is implemented either in TypeScript or Python using the official SDKs, showing real-world patterns for tool integration (e.g. wrapping an API or database behind the MCP interface). Studying these implementations is invaluable for learning best practices and the range of capabilities MCP supports.
“Awesome MCP Servers” Curated List – The community has also created an Awesome MCP list that curates notable MCP servers and resources (GitHub - appcypher/awesome-mcp-servers: Awesome MCP Servers - A curated list of Model Context Protocol servers) (GitHub - appcypher/awesome-mcp-servers: Awesome MCP Servers - A curated list of Model Context Protocol servers). This includes both production-ready integrations and experimental servers that extend AI models with new abilities (file access, databases, web search, etc.). The repo also tracks which client applications support MCP – beyond Anthropic’s Claude, other AI platforms have added MCP compatibility. For example, as of 2025: Claude Desktop, the Zed code editor, Sourcegraph Cody, Continue (VS Code extension), LibreChat, Cursor AI, and others can act as MCP hosts (clients) (GitHub - appcypher/awesome-mcp-servers: Awesome MCP Servers - A curated list of Model Context Protocol servers) (GitHub - appcypher/awesome-mcp-servers: Awesome MCP Servers - A curated list of Model Context Protocol servers). This means you can use those apps to interface with any MCP server. Exploring the Awesome list is a great way to discover community-driven MCP tools and to stay updated on the evolving ecosystem of integrations.
Microsoft Integrations – MCP’s rapid adoption is evident from Microsoft’s involvement. Microsoft’s Dev Blogs reported that products like GitHub Copilot X (VS Code’s agent mode), Copilot Studio, and Semantic Kernel have added support for MCP (Microsoft partners with Anthropic to create official C# SDK for Model Context Protocol - Microsoft for Developers). Microsoft and community developers have created MCP servers for services like GitHub (code repo) and Playwright (browser), enabling those tools to be used by LLMs through the protocol (Microsoft partners with Anthropic to create official C# SDK for Model Context Protocol - Microsoft for Developers) (Microsoft partners with Anthropic to create official C# SDK for Model Context Protocol - Microsoft for Developers). This cross-vendor support (even OpenAI’s own Agents SDK, noted below, supports MCP) underscores MCP’s emerging role as a standard interface for connecting AI assistants with external systems.

Educational Blog Posts and Articles

InfoQ News Overview (Dec 2024) – InfoQ published a concise news article “Anthropic Publishes Model Context Protocol Specification for LLM App Integration” shortly after MCP’s release (Anthropic Publishes Model Context Protocol Specification for LLM App Integration - InfoQ) (Anthropic Publishes Model Context Protocol Specification for LLM App Integration - InfoQ). It explains in plain terms how MCP tackles the integration complexity by standardizing interactions between M models and N tools (Anthropic Publishes Model Context Protocol Specification for LLM App Integration - InfoQ). The article quotes Anthropic’s commitment to developing MCP collaboratively and highlights that SDKs (Python, TS) and many community-contributed servers were already available (Anthropic Publishes Model Context Protocol Specification for LLM App Integration - InfoQ). It also summarizes technical details from the spec: for example, that MCP uses JSON-RPC and defines server primitives (Prompts, Resources, Tools) and client primitives (Roots, Sampling) (Anthropic Publishes Model Context Protocol Specification for LLM App Integration - InfoQ) (Anthropic Publishes Model Context Protocol Specification for LLM App Integration - InfoQ). If you need a high-level summary of MCP’s goals and components, this 2-minute read is a great start.
Morgan Linton’s Introduction (Nov 2024) – Tech blogger Morgan Linton wrote a Medium post titled “Anthropic announced MCP yesterday, and you should care, a lot.” This informal article demystifies MCP’s purpose in very simple terms. Linton describes MCP as “a way to connect AI assistants (think agents) to a system where data is stored”, giving the example of hooking an AI agent up to a GitHub repository so it can use that data (Anthropic announced Model Context Protocol (MCP) yesterday, and you should care, a lot | by Morgan Linton | Medium). He emphasizes that MCP was not built overnight, but by open-sourcing it, Anthropic enabled anyone to run their own MCP server and plug in their custom data. The post lists the three components launched (spec + SDKs, Claude Desktop support, open-source servers) (Anthropic announced Model Context Protocol (MCP) yesterday, and you should care, a lot | by Morgan Linton | Medium) and argues that every team (from engineering to sales) could benefit from using MCP to leverage their data sources (e.g. Salesforce, Slack, GitHub) via AI (Anthropic announced Model Context Protocol (MCP) yesterday, and you should care, a lot | by Morgan Linton | Medium). This piece is useful for understanding why MCP is a big deal for the future of work, in a non-technical, enthusiastic tone.
Arize AI Blog (Mar 2025) – Arize (an AI observability company) published “Model Context Protocol” on their blog, which is a detailed breakdown of MCP’s significance, based on a webinar talk by SallyAnn DeLucia (Model Context Protocol - Arize AI). The article frames MCP as “groundbreaking” for transforming isolated AI models into capable, context-aware agents by giving them seamless access to external knowledge (Model Context Protocol - Arize AI). It discusses key benefits of MCP, such as improved context retention across interactions, better interoperability in complex workflows, and enabling AI to perform more nuanced, real-world tasks. The post recaps the current state of AI integrations (models are limited by lack of real-time data and siloed systems) (Model Context Protocol - Arize AI) and then introduces MCP as “a universal standard for AI connectivity” that “replaces fragmented integrations with a single protocol”, essentially a “USB for AI” that standardizes how AI connects to any external function or dataset (Model Context Protocol - Arize AI). The Arize blog is great for grasping the bigger picture of how MCP can change AI application design, and it includes examples and quotes from the webinar that illustrate MCP’s potential impact in enterprises.
deepset Blog (Apr 2025) – In “Understanding the Model Context Protocol (MCP)”, deepset (creators of the Haystack QA framework) explain MCP’s architecture and industry momentum (Understanding the Model Context Protocol (MCP) | deepset Blog) (Understanding the Model Context Protocol (MCP) | deepset Blog). They note that Anthropic released MCP in November 2024 and that hundreds of MCP servers quickly emerged in the developer community (Understanding the Model Context Protocol (MCP) | deepset Blog). Interestingly, they mention that even Anthropic’s rival OpenAI is officially adopting MCP, along with companies like AWS and GitHub (Understanding the Model Context Protocol (MCP) | deepset Blog). This refers to OpenAI integrating MCP support into their tools (see the Agents SDK below). The blog compares MCP’s role to that of REST APIs in web development – prior to MCP, connecting an AI to various data sources was done in ad-hoc ways, but now MCP provides “a consistent, structured format” for these interactions, allowing interoperability across different platforms (Understanding the Model Context Protocol (MCP) | deepset Blog). The article continues to describe how MCP works and why that makes building AI applications faster and more maintainable (Understanding the Model Context Protocol (MCP) | deepset Blog). This is a valuable read for a technical yet accessible perspective, and it draws parallels to known software patterns (making it easier to contextualize MCP’s design).
Medium Deep Dive for Developers (Mar 2025) – A Medium article by Amanatullah, “Anthropic’s MCP: A Deep Dive for Developers,” provides a thorough technical walkthrough of MCP’s design and how it differs from other approaches (Anthropic’s Model Context Protocol (MCP): A Deep Dive for Developers | by Amanatullah | Mar, 2025 | Medium) (Anthropic’s Model Context Protocol (MCP): A Deep Dive for Developers | by Amanatullah | Mar, 2025 | Medium). It explains the client-server roles (host process, MCP clients, MCP servers) in detail, with examples like the Claude desktop app as the host spawning clients for each server connection (Anthropic’s Model Context Protocol (MCP): A Deep Dive for Developers | by Amanatullah | Mar, 2025 | Medium) (Anthropic’s Model Context Protocol (MCP): A Deep Dive for Developers | by Amanatullah | Mar, 2025 | Medium). One valuable section is “MCP vs. OpenAI Function Calling: Key Differences”. The author notes that OpenAI’s function-calling feature (which lets a model format queries to call a tool) is vendor-specific and limited in scope, whereas MCP is a broader, model-agnostic protocol standardizing the entire interaction lifecycle (Anthropic’s Model Context Protocol (MCP): A Deep Dive for Developers | by Amanatullah | Mar, 2025 | Medium) (Anthropic’s Model Context Protocol (MCP): A Deep Dive for Developers | by Amanatullah | Mar, 2025 | Medium). MCP not only covers calling functions, but also discovering available tools, sharing data context, handling responses, etc., in a uniform way across any AI system. The article points out that with function calling, each model might have a different JSON format for tools (and frameworks like LangChain are used to adapt them), but “MCP, on the other hand, is designed to be model-agnostic and an open standard,” meaning any LLM implementing an MCP client can interact with any MCP server (Anthropic’s Model Context Protocol (MCP): A Deep Dive for Developers | by Amanatullah | Mar, 2025 | Medium). For developers who want a comparative understanding of integration options, this deep dive is very informative. (Note: The article also covers many core MCP concepts, so it’s a useful learning resource even if you’re not specifically interested in function calling.)
Docker Blog (Dec 2024) – Docker’s engineering blog featured “Simplifying Building AI Apps with Anthropic Claude Desktop and Docker”, which discusses using Docker containers to distribute and run MCP servers (The Model Context Protocol: Simplifying Building AI apps with Anthropic Claude Desktop and Docker | Docker) (The Model Context Protocol: Simplifying Building AI apps with Anthropic Claude Desktop and Docker | Docker). This post acknowledges that while MCP is a great new standard for connecting to data/tools (The Model Context Protocol: Simplifying Building AI apps with Anthropic Claude Desktop and Docker | Docker), setting up various MCP servers can be tricky due to different environment dependencies. Docker solves this by containerizing MCP servers for consistency across dev and prod. The blog briefly reiterates what MCP is: an open protocol for LLM apps to integrate external data and tools (The Model Context Protocol: Simplifying Building AI apps with Anthropic Claude Desktop and Docker | Docker), enabling AI apps to retrieve external data, call third-party services, or interact with local filesystems via standardized interfaces (The Model Context Protocol: Simplifying Building AI apps with Anthropic Claude Desktop and Docker | Docker) (The Model Context Protocol: Simplifying Building AI apps with Anthropic Claude Desktop and Docker | Docker). It then gives examples of containerizing specific MCP servers. Importantly, it highlights how MCP introduces “tool discovery” (LLMs can learn what tools are available) and “tool invocation” (LLMs can execute tools with proper context/arguments) as first-class capabilities (The Model Context Protocol: Simplifying Building AI apps with Anthropic Claude Desktop and Docker | Docker). This blog is a practical angle on MCP – useful if you’re thinking about deploying MCP servers in a reliable way, or just to reinforce your understanding of MCP’s role in packaging AI integrations.
Additional Articles – There are many other write-ups and discussions about MCP. For example, Descope’s blog published a guide explaining MCP’s architecture and security implications, and Cisco Outshift and IBM researchers have written about MCP in the context of emerging agent communication standards (ACP, etc.). There are also community-curated pieces like a HuggingFace blog that compiled Reddit discussions about MCP’s impact (MCP is All You Need: The Future of AI Interoperability - Hugging Face). As you deepen your expertise, exploring these resources can provide niche insights (e.g. enterprise security considerations, or how MCP compares to other research prototypes), but the above list covers the most prominent and current resources to build a strong foundation.

Tutorials and Hands-On Guides

One of the best ways to learn MCP is by following tutorials and examples that demonstrate how to implement it in practice:

Anthropic’s Quickstart Example – The official documentation includes a Quickstart that walks through building a simple MCP server (in Python) and using it with Claude. For instance, the weather data example shows how to create an MCP server that fetches weather info from a web API and exposes it as a “Weather Tool” (Anthropic Publishes Model Context Protocol Specification for LLM App Integration - InfoQ). The Claude desktop app (which has an MCP client built-in) can then query this server to answer user questions about the weather. This illustrates the basic pattern: wrap an external API as a tool on an MCP server, and the LLM can call it as needed. Following the Quickstart step-by-step is highly recommended – it covers setting up the environment, writing a minimal server, and testing it via Claude, giving you a concrete mental model of MCP’s workflow.
DataCamp Tutorial (Oct 2024) – DataCamp published “Model Context Protocol (MCP): A Guide With Demo Project”, which is a hands-on tutorial for building a custom MCP server (Model Context Protocol (MCP): A Guide With Demo Project | DataCamp) (Model Context Protocol (MCP): A Guide With Demo Project | DataCamp). In this guide, the author creates an MCP-powered Pull Request (PR) review server that integrates with Claude Desktop. The server connects to both GitHub and Notion: it fetches pull request details and code diffs from GitHub, and also logs analysis notes to Notion. The tutorial explains each step: defining the server’s capabilities (e.g. a tool to fetch PR info), using the Python MCP SDK (mcp[cli] package) to implement the server, and then launching Claude to interact with it (Model Context Protocol (MCP): A Guide With Demo Project | DataCamp) (Model Context Protocol (MCP): A Guide With Demo Project | DataCamp). Throughout, it emphasizes how MCP standardizes the communication between Claude (the client) and the custom server, making the integration modular and scalable (Model Context Protocol (MCP): A Guide With Demo Project | DataCamp) (Model Context Protocol (MCP): A Guide With Demo Project | DataCamp). This is an excellent real-world example combining multiple data sources. By the end, you’ll see how Claude can dynamically retrieve code context and perform a PR review by calling the MCP server’s tools – a powerful demonstration of MCP’s utility for developer workflows.
Building MCP Servers with LLMs (Anthropic Guide) – An interesting tutorial on the official site, “Building MCP with LLMs”, shows how you can leverage an AI (like Claude itself) to speed up development of MCP servers (Building MCP with LLMs - Model Context Protocol) (Building MCP with LLMs - Model Context Protocol). It suggests a workflow where you feed Claude the MCP documentation and then prompt it to generate code for a server based on your requirements. For example, “Build an MCP server that connects to my company’s PostgreSQL database, exposes table schemas as resources, provides a query tool, and includes prompts for common analytics” (Building MCP with LLMs - Model Context Protocol) (Building MCP with LLMs - Model Context Protocol). Claude can then draft the code which you can iterate on. This guide is meta — using an AI to build AI tools — but it’s a great way to explore MCP’s capabilities and the SDK usage more interactively. It also introduces the MCP Inspector and debugging tips, which help in testing your server’s behavior (these tools let you simulate an MCP client to call your server’s functions and ensure it’s working correctly).
Community Tutorials (.NET example) – With the advent of the C# SDK, community members have blogged about MCP integration in the .NET ecosystem. For instance, a developer wrote “Building an Anthropic MCP Server with .NET” on their blog (PowerShellEngineer), detailing how to set up an MCP server in C# to interface with Microsoft 365 data. This kind of tutorial can be useful if you’re specifically working in a certain tech stack. Similarly, some Medium posts (like “Working with Anthropic’s MCP – Part 1”) chronicle personal experiences learning MCP, which can offer tips and gotchas. As MCP is evolving, keep an eye on developer forums and blogs for such walk-throughs, especially in languages or domains of your interest.
Docker & Deployment Guides – As mentioned, the Docker blog shows how to containerize MCP servers for ease of deployment (The Model Context Protocol: Simplifying Building AI apps with Anthropic Claude Desktop and Docker | Docker) (The Model Context Protocol: Simplifying Building AI apps with Anthropic Claude Desktop and Docker | Docker). If your goal is to integrate MCP into a production environment, looking into Docker or Kubernetes setups is worthwhile. The Awesome MCP Servers list also references a CLI tool called mcp-get for installing/managing servers, which might streamline running popular servers locally (GitHub - appcypher/awesome-mcp-servers: Awesome MCP Servers - A curated list of Model Context Protocol servers). These practical guides ensure that once you’ve developed an MCP integration, you know how to reliably run it in various environments.

By following a couple of these tutorials, you’ll get a hands-on understanding of how MCP works: you’ll write some code, run an MCP server, connect a client (Claude or others) to it, and witness the model calling your custom tools. This experience is crucial to solidify the concepts learned from documentation and articles.

Video Workshops and Talks

For those who prefer learning via videos or want to see live demos, there are a few excellent video resources on MCP:

“Building Agents with Model Context Protocol” (AI Engineer Workshop) – This is a full 2-hour workshop on YouTube (from the AI Engineer channel) featuring Mahesh Murag of Anthropic, dedicated to MCP. It’s listed on the official site as “MCP Workshop (Video, 2hr)” (Introduction - Model Context Protocol). In this workshop, the presenter goes through the motivations behind MCP, the technical breakdown of how it works, and live-codes examples of building an AI agent that uses MCP for tool access. Key portions include demonstrating how an MCP client (Claude) discovers available tools from a server and how to implement new server capabilities. This is arguably the most in-depth video on MCP available, given the length and involvement of an Anthropic team member. If you have the time, watching (and maybe coding along with) this workshop will greatly reinforce your understanding.
Conferences and Talks – Anthropic has started discussing MCP in developer conferences. In May 2025, Anthropic held their first dev conference “Code with Claude,” which included sessions on MCP’s roadmap and best practices (Code with Claude - Anthropic's First Developer Conference \ Anthropic). While the content of that event isn’t fully public as of this writing, keep an eye out for recorded talks or slide decks from it – they likely contain up-to-the-minute insights (e.g. new features in MCP, lessons from real adopters, etc.). Additionally, AI community conferences (like QCon AI) often feature talks on building AI systems with tools; MCP has been referenced in broader talks about AI agent architectures. For example, some ICML 2024 workshops and WandB’s series have touched on standardized tool-use protocols like MCP (sometimes alongside discussions of OpenAI’s plugins or LangChain). As MCP becomes more widely adopted, expect more such talks to pop up on YouTube or SlideShare.
Short Explainer Videos – A few tech YouTubers and AI enthusiasts have created shorter videos comparing MCP with other protocols. For instance, there’s a 12-minute video “Google A2A vs Anthropic MCP: 3 Critical Differences” that visually breaks down the distinctions between these protocols. Another titled “You Need to Know: Google Agent2Agent (A2A) vs MCP” (by Chris Hay) explains how one might use both in building AI agents. These can serve as quick refreshers or clarifications on specific points, though the quality varies. They might be useful after you have a basic understanding, to reinforce knowledge or catch nuances you missed. (If you do watch these, always cross-reference with official info since community-made videos can occasionally oversimplify.)

(A2A and MCP: Start of the AI Agent Protocol Wars? - Koyeb) Diagram: Google’s Agent-to-Agent (A2A) protocol connects multiple intelligent agents. It standardizes how agents discover each other (via “agent cards”) and communicate tasks and messages directly (A2A and MCP: Start of the AI Agent Protocol Wars? - Koyeb) (A2A and MCP: Start of the AI Agent Protocol Wars? - Koyeb). A2A is focused on multi-agent collaboration, not on accessing external tools.

Google A2A (Agent-to-Agent) – Announced in April 2025, A2A is an open protocol from Google that standardizes how AI agents communicate with each other and coordinate tasks (A2A and MCP: Start of the AI Agent Protocol Wars? - Koyeb) (A2A and MCP: Start of the AI Agent Protocol Wars? - Koyeb). It addresses scenarios where you have multiple agents (with potentially different specialties or running on different platforms) that need to work together. For example, one agent could delegate a sub-task to another agent using A2A, or two agents could share information to solve a problem. A2A introduces concepts like Agent Cards (JSON descriptions an agent publishes about its capabilities and endpoints) for discovery (A2A and MCP: Start of the AI Agent Protocol Wars? - Koyeb), and supports communication patterns such as request-response, server-sent events (SSE) streams, and push notifications for long-running tasks (A2A and MCP: Start of the AI Agent Protocol Wars? - Koyeb). How it compares to MCP: Google explicitly states that “A2A is an open protocol that complements Anthropic’s MCP, which provides helpful tools and context to agents.” (A2A and MCP: Start of the AI Agent Protocol Wars? - Koyeb). In other words, A2A is not meant to replace MCP but to cover a different layer of the AI ecosystem. Where MCP connects agents to tools/data, A2A connects agents to other agents. An easy analogy: if you imagine a team, MCP would let each team member (agent) use various tools and databases, while A2A would be the communication channel that team members use to chat and coordinate with each other. They can be used together – for instance, one could build a system where several agents each have access to tools via MCP, and these agents then collaborate via A2A to achieve a larger goal (A2A and MCP: Start of the AI Agent Protocol Wars? - Koyeb) (A2A and MCP: Start of the AI Agent Protocol Wars? - Koyeb). (Indeed, Google’s docs provide a scenario of an auto-repair shop with multiple agent “employees”: MCP enables each agent to use structured tools like lifting a car or checking inventory, while A2A allows the agents and the human user to converse and plan the repair (A2A and MCP: Start of the AI Agent Protocol Wars? - Koyeb).) The key takeaway is that A2A vs MCP is not an either/or choice – they address different needs and can complement each other. MCP focuses on context injection and tool use, A2A on agent interaction and teamwork. Many commentators see them as two parts of a future “AI agent stack.” (A2A and MCP: Start of the AI Agent Protocol Wars? - Koyeb) (MCP vs A2A: Comparing AI Agent Protocols for Modern Enterprise)

(A2A and MCP: Start of the AI Agent Protocol Wars? - Koyeb) Diagram: Anthropic’s MCP follows a host/client–server architecture. An AI host application (with an MCP client, e.g. Claude or another chat interface) can connect to multiple MCP servers (each exposing some tool, database, or service). The servers interface with either local data sources (like files or databases) or remote services (via APIs), giving the AI model controlled access to those resources (A2A and MCP: Start of the AI Agent Protocol Wars? - Koyeb) (A2A and MCP: Start of the AI Agent Protocol Wars? - Koyeb).

MCP vs Agent Communication Protocols (General) – Apart from Google’s A2A, there are other research initiatives like IBM’s Agent Communication Protocol (ACP) (used in their BeeAI project) that also define agent-to-agent messaging standards. These share similar goals with A2A in enabling multi-agent systems to talk to each other in an interoperable way (MCP and ACP: Decoding the language of models and agents) (MCP, ACP, A2A, Oh my! - WorkOS). The rise of A2A and ACP, inspired by MCP’s success (MCP vs. A2A: Friends or Foes? - by Aurimas Griciūnas), has led to what some call the era of “AI Agent Protocol Wars” – multiple groups trying to set the standards for how AI agents will connect and orchestrate. However, MCP stands out in focusing squarely on the agent-to-tool/domain integration problem. Anthropic deliberately scoped MCP to standardize how an AI gets the context it needs (from databases, knowledge bases, APIs, etc.) and how it can perform actions in the world (tools/commands), without prescribing multi-agent coordination. This is why MCP’s primitives (prompts, resources, tools) map well to a single agent’s interaction with its environment, whereas protocols like A2A/ACP deal with agent-agent dialogue, task delegation, and maintaining consistency of state between agents (MCP vs. A2A: Friends or Foes? - by Aurimas Griciūnas) (MCP vs. A2A: Friends or Foes? - by Aurimas Griciūnas). As an expert, you should recognize that MCP is complementary to any agent-to-agent protocol – in a complex system, an agent might use MCP to access information and also use A2A or ACP to communicate with peer agents. It’s analogous to how in networking, we have protocols for different layers (HTTP for application data transfer vs. SMTP for email vs. TCP/IP for transport) – here MCP is a high-level application protocol for tool use, while A2A/ACP are high-level protocols for agent interaction.

MCP vs Plugins/Function Calling – Before MCP and A2A, the common way to extend an LLM’s capabilities was via proprietary plugins or function-calling interfaces. For example, OpenAI’s plugin ecosystem and function calling JSON format allow a model like GPT-4 to call external APIs by formatting a JSON object in its response. The Medium deep dive (Anthropic’s Model Context Protocol (MCP): A Deep Dive for Developers | by Amanatullah | Mar, 2025 | Medium) (Anthropic’s Model Context Protocol (MCP): A Deep Dive for Developers | by Amanatullah | Mar, 2025 | Medium) and discussions on Hacker News highlight the differences here. MCP can be seen as a more general and standardized superset of what function calling offers. OpenAI’s function calls are tied to specific model APIs and require the developer to host a web service following OpenAI’s schema. By contrast, MCP provides a model-agnostic RPC mechanism: any model or client that speaks MCP can call any MCP server’s tools, regardless of vendor. It also formalizes things beyond just function invocation – e.g. tool discovery (the client can ask the server “what tools and resources do you have?” which in MCP is done via calls like list_tools), and streaming responses (MCP natively supports streaming via WebSockets or SSE transports). Additionally, MCP’s concept of Resources (passing chunks of data context) has no direct analogue in simple function calling – it’s like an extension that lets an AI proactively fetch relevant data (documents, schema, etc.) to incorporate into its prompt, not only react to user queries. In summary, while OpenAI’s function calling was an important step (and is even used within MCP servers to implement some tools), MCP operates at a higher level of abstraction and interoperability. It turns tool use into a standardized dialogue between an AI and an external system. One could imagine a future where even OpenAI’s models natively support MCP – in fact, deepset’s blog implies some level of OpenAI adoption (Understanding the Model Context Protocol (MCP) | deepset Blog), and OpenAI’s own Agents SDK has built-in support for connecting to MCP servers (Model context protocol (MCP) - OpenAI Agents SDK) (Model context protocol (MCP) - OpenAI Agents SDK). This indicates that rather than invent a completely new system, OpenAI might leverage MCP for the “tools and context” layer while focusing their efforts on other aspects of agent behavior.

When to Use What – To wrap up the comparison: if you are building an AI agent that needs internet tools, databases, or enterprise data, MCP is currently the leading open standard to implement that integration (across many model providers). If you are orchestrating multiple AI agents or specialized workers, an agent-comm protocol like A2A or ACP might also come into play, but you would likely still use MCP within each agent for its tool usage. Traditional function calling or proprietary plugin frameworks could be used for simpler cases or specific platforms, but they tend to lock you into one vendor’s ecosystem. MCP, being open and model-agnostic, gives flexibility – you could switch the language model (say from Claude to another MCP-compliant model) without rewriting your tool integration. This interoperability is why many observers say MCP (and A2A) are “the future of AI integration”, akin to how HTML/JS standardized web app platforms. By understanding how MCP compares with alternatives, you can make informed design choices and also appreciate the significance of MCP’s design in the context of industry trends.

Conclusion:

With the above resources, you have a comprehensive learning path to become an MCP expert. Start with the official documentation to grasp what MCP is and how it works. Use the blog posts and articles to enrich your understanding of why MCP matters and how it’s being used in the wild. Then get your hands dirty with tutorials – build a toy MCP server or two, use Claude or another client to interact with them, and perhaps even containerize and deploy one. Alongside, watch the workshop video to reinforce those concepts visually. As you progress, compare MCP with other emerging protocols like A2A to contextualize its place in the ecosystem. And throughout, engage with the community – it’s one of the fastest ways to learn the subtle details and get help or inspiration.

By prioritizing up-to-date (2024–2025) sources, this guide ensures you’re learning the current state of MCP – a rapidly growing standard that many believe will form the backbone of tomorrow’s AI agents. Armed with this knowledge, you’ll be well-equipped to implement MCP in your own projects and contribute to the conversation shaping its future. Happy learning, and welcome to the world of “connected” AI agents!