The Model Context Protocol (MCP) by Anthropic: Origins, functionality, and impact



Table of contents

Origins and motivations for MCP

How MCP works: A technical breakdown

MCP Host: The host is the AI-powered application or agent environment (for example, the Claude desktop app, an IDE plugin, or any custom LLM-based app). The host is what the end-user interacts with, and it can connect to multiple MCP servers at once (e.g. one server for email, one for a database).
MCP Client: The client is an intermediary that the host uses to manage each server connection. Each MCP client handles the communication to one MCP server, keeping them sandboxed for security. The host spawns a client for each server it needs to use, maintaining a one-to-one link.
MCP Server: The server is a program (usually external to the model) that implements the MCP standard and provides a specific set of capabilities – typically a collection of tools, access to data resources, and predefined prompts related to some domain. An MCP server might interface with a database, a cloud service, or any data source; Anthropic and the community have released servers for Google Drive, Slack, GitHub, databases like Postgres/SQLite, web browsers (via Puppeteer), and more.

Communication and Primitives

Server-side primitives: Prompts, Resources, and Tools. A Prompt in MCP is a prepared instruction or template that can guide the model (similar to a stored prompt or a macro). A Resource is structured data the server can send to enrich the model’s context (for example, a document snippet, a code fragment, or any info to include in the prompt). A Tool is an executable function or action the model can invoke via the server (e.g. a database query, a web search, or posting a message to Slack). These primitives let the server present capabilities to the AI – essentially telling the model “here are extra instructions you can use, data you can pull in, and actions you can take.”
Client-side primitives: Roots and Sampling. A Root represents an entry-point into the host’s filesystem or environment that the server might access if permitted (for instance, giving a server access to certain local files). Sampling is a mechanism that allows the server to request the host AI to generate a completion given some prompt. This is a more advanced feature: it means a server can ask the model to think or write something mid-process, enabling complex multi-step reasoning (e.g. an agent on the server side could call back to the model for sub-tasks). Anthropic cautions that Sampling should always require human approval, to avoid runaway self-prompts.

Capability Discovery: The MCP client first asks the server to describe what it offers – i.e. it fetches the list of available tools, resources, or prompt templates that the server can provide. The AI model (via its host app) is made aware of these capabilities.
Augmented Prompting: The user’s query (and other context) is sent to the AI model along with descriptions of the server’s tools/resources. In effect, the model now “knows” what it could do via the server. For example, if the user asks “What’s the weather tomorrow?”, the prompt to the model includes a description of a “Weather API tool” that the server exposes.
Tool/Resource Selection: The AI model analyzes the query and the available MCP tools/resources, and decides if using one is necessary. If so, it responds in a structured way (per the MCP spec) indicating which tool or resource it wants to use. In our weather example, the model might decide to call the “Weather API” tool provided by the server to get up-to-date info.
Server Execution: The MCP client receives the model’s request and invokes the corresponding action on the MCP server (e.g. executes the weather API call through the server’s code). The server performs the action – such as retrieving data from a database or calling an external API – and then returns the result to the client.
Response Generation: The result from the server (say, the weather forecast data) is handed back to the AI model via the client. The model can now incorporate this data into its answer. It then generates a final response to the user (e.g. “Tomorrow’s forecast is 15°C with light rain.”) based on both its own knowledge and the freshly fetched information. The user sees an answer that was enriched by the model’s ability to seamlessly pull in external info during the conversation.

Security and control

Applications and Use Cases of MCP

Enterprise Data Assistants: One major impact area is enterprise knowledge retrieval – AI assistants that can securely access company data, documents, and services to answer questions or automate tasks. With MCP, a corporate chatbot could query multiple internal systems in one conversation: for example, pulling an employee’s HR record from a database (via a database connector), checking project details from a project management tool, and even posting an update in Slack – all standardized through MCP. Early adopters have built connectors for popular enterprise tools like Google Drive (for document search), Slack (for messaging and knowledge in channels), and various databases. Anthropic provides out-of-the-box MCP servers for these systems, so an AI like Claude can immediately retrieve a file from Drive or a conversation from Slack when needed. This is expected to significantly improve customer support bots, HR assistants, and other internal AI agents, which can now draw on up-to-date internal knowledge bases rather than a static training snapshot.
Software Development and Coding Agents: MCP has quickly gained traction in developer tooling. Coding assistants (like GitHub Copilot, Replit’s AI, Sourcegraph’s Cody, etc.) benefit greatly from being able to fetch code context, documentation, and even take actions in repositories. Companies like Sourcegraph, Zed, Replit, and Codeium have started integrating MCP to make their AI coding features richer. For instance, Sourcegraph’s Cody announced support for MCP, meaning it can pull in additional “context outside the code” from your repositories or issues to better answer queries. An IDE with MCP integration could let the AI read relevant files from the project, execute build/test commands, or search version history when a developer asks a question about the code. This yields more nuanced, accurate code suggestions because the AI isn’t working blindly – it can consult the actual codebase, commit logs, or documentation through MCP connectors. It essentially enables AI pair programmers that truly understand the project’s context. As an example, one of the reference MCP servers allows an AI to run SQL queries on a local SQLite database, which a coding assistant could use to fetch test data or configurations during a session.
Personal Productivity and Agents: MCP can power personal AI agents that manage tasks across apps. Imagine a virtual assistant that can, in one workflow, read your email, add events to your calendar, update a to-do list, and control smart devices. Traditionally this would require multiple platform-specific integrations. With MCP, one could write or obtain servers for Gmail, Google Calendar, task managers, etc., and have a single AI agent coordinate them. A demo by the community showed a “Gmail agent” built with MCP that can read and draft emails via a Gmail connector. Another example uses a Puppeteer MCP server to let an AI navigate and scrape websites, essentially giving it a web browser tool. Because MCP standardizes how these abilities are exposed, the same AI model could use the same browsing tool, the same email tool, or any others, as long as it supports the MCP client. This hints at a future of agentic AI that can perform complex sequences (with appropriate oversight) – Anthropic explicitly notes MCP will help build agentic systems that handle mechanical tasks so humans can focus on creative work.
Multi-Modal and Data Analysis Tools: Beyond text-based data, MCP can integrate AI with other modalities and data analysis. For instance, an MCP server could interface with a spreadsheet or analytics API, allowing the AI to pull a chart or compute statistics when asked a question about data. Or it could connect to a cloud monitoring system like Sentry or Cloudflare to fetch real-time metrics or logs – indeed, platforms like Cloudflare and Sentry have been cited as exploring MCP. This would let AI assistants help with IT and DevOps tasks (e.g. “Is our server experiencing errors?” → AI uses Sentry MCP tool to retrieve the latest error logs). By making these integrations easier, MCP could bring AI assistance into domains like finance (querying financial databases), healthcare (fetching data from health record systems with proper compliance), or education (accessing course content or student data), always through that common protocol layer.

MCP vs. existing context management methods

Versus Retrieval-Augmented Generation (RAG) and Vector Search: Retrieval-Augmented Generation is a popular technique where an AI model is paired with a knowledge base: relevant documents are fetched (often via vector embeddings or keyword search) and simply appended to the prompt. This does provide external knowledge, but MCP offers more structure and flexibility. In a typical RAG pipeline, the retrieved text snippets get lumped into the model’s prompt as unstructured context. With MCP, by contrast, you can slot data into distinct, labeled context layers or as Resource primitives. For example, using RAG inside MCP, you might retrieve text and then place it in an MCP block labeled “Retrieved Documents” rather than mixing it with everything else. This means each piece of context (company policy, user query, retrieved info, etc.) stays organized in the prompt with its own role, which helps the model interpret and prioritize information better. Additionally, MCP doesn’t replace RAG – it augments it: you might still use a vector database to find relevant text, but then feed that text through MCP as a resource, alongside other tools and instructions . The benefit is maintainability and clarity: instead of managing one giant concatenated prompt, developers can update or swap out one layer (say, update the “policy docs” resource) without disturbing others.
Versus Monolithic Prompts or Fine-Tuning: Before protocols like MCP, if you wanted an AI to follow certain guidelines or have domain knowledge, you often had two choices: cram everything into a monolithic prompt or fine-tune the model. Both have downsides. Huge prompts with all rules and context become unwieldy and costly, and any change (like updated guidelines) means revising the whole prompt. Fine-tuning, on the other hand, is time-consuming and inflexible – updating the model for each new piece of data can take days and risks side effects. MCP provides a more dynamic middle ground: it allows modular prompt segments. You might have one MCP prompt primitive that always supplies the latest policy guidelines, another that contains the user’s conversation history, and a resource that has relevant knowledge fetched on the fly. This layered approach means the model’s behavior can be adjusted by tweaking one layer’s content or instructions, without needing to retrain or rewrite everything. In practice, this saves time and tokens – instead of feeding the entire company handbook every time, the AI gets a specific “policy” context only when needed, via the MCP server. It’s a more scalable approach to managing context and instructions for complex AI applications.
Versus Ad-hoc Tool Integration (Function Calling and Plugins): Recent LLMs (like OpenAI’s GPT-4) introduced function calling or plugin mechanisms, where the model can call developer-defined functions or use web APIs. These are powerful, but each AI provider had its own implementation (OpenAI’s JSON function call format, plugins with OpenAPI specs, etc.), and each tool still requires custom setup per model. MCP generalizes this by standardizing how any tool is presented and invoked, across models and platforms. In effect, MCP can be seen as a superset of the “tool use” idea – not only does it allow tool calls (similar to function calling) with its Tool primitives, but it also handles sending data context (Resource) and preset prompts. So rather than writing unique plugin code for OpenAI, another for Claude, another for Llama, etc., developers and tool makers can target MCP and potentially work with all MCP-compatible models. This interoperability is a key distinction. Furthermore, Anthropic’s team decided that tools alone weren’t sufficient; they explicitly kept Prompts and Resources as first-class concepts in MCP to capture use cases where injecting or templating context is better than calling a function. The presence of these separate primitive types allows MCP to cover scenarios that pure function APIs don’t, like providing advisory instructions or reference texts that guide the model’s answer without being an “action” per se.

Industry reactions and expert opinions to MCP

Future implications of MCP

Standardized Ecosystem: If MCP gains broad adoption, we could see a future where AI models and tools are largely interoperable. An organization might build a suite of MCP servers for their internal systems – and any AI model or agent that speaks MCP (whether from Anthropic, OpenAI, or open-source) could immediately plug into that suite. This decouples the choice of AI model from the integrations. Companies could swap out or upgrade their language model without redoing all the connectors for data and actions, as long as both old and new models support MCP. Such a scenario is powerful for AI evolution: it encourages experimentation (try a new model easily) and avoids vendor lock-in on integrations. It’s analogous to how any web browser can talk to any website thanks to common standards. Researchers might focus on improving model reasoning and let MCP handle feeding the model the right information. In turn, tool developers only need to make one MCP interface that can serve many AIs. This network effect could accelerate AI deployment across industries if it becomes as universal as hoped.
Enhanced Agentic AI Behavior: MCP lays important groundwork for more autonomous AI agents. Today’s AI assistants are often single-turn Q&A or require the user to provide context. With MCP, an AI can actively gather its own context by calling tools or querying resources mid-conversation. This means future AI agents could handle complex tasks with minimal user input – for example, a business analyst bot that, when asked for a report, on its own pulls data from databases, crunches numbers in a spreadsheet, and generates a summary. We’re already seeing early steps: MCP’s Sampling primitive even allows an AI-driven server to request further AI computations, essentially letting an agent chain multiple reasoning steps (albeit with human oversight as a safeguard). As researchers explore AI planning and reasoning, MCP provides a ready-made channel for connecting those plans to real-world actions and data. It could become a key piece in building AI systems that are goal-directed and context-aware, not just stateless chatbots. Of course, this raises the importance of governance – ensuring these agents act safely. But MCP’s design already considers that, by requiring user permission and keeping humans “in the loop” for sensitive operations. In the long run, one can imagine AI orchestrators that use MCP to interface with everything from cloud databases to IoT devices, effectively becoming general-purpose agents operating under human-set policies.
Shaping AI Research Focus: With a tool like MCP handling external knowledge access, AI researchers might place slightly less emphasis on ever-larger training datasets or parameter counts, and more on how models utilize external knowledge. If an AI can fetch up-to-the-minute information, the need to stuff every fact into its trained weights diminishes. This could lead to more efficient systems: smaller base models that rely on MCP connectors for detailed data. It also encourages a modular approach to AI system design – separating the reasoning engine (the model) from the knowledge sources and action modules. Academically, this aligns with ideas of compositional AI and grounding models in real-world operations. MCP could thus spur research into how to best format context for models, how to let models decide when to use a tool, and how to maintain long-lived session state across tasks. By providing a concrete protocol for these interactions, MCP offers a platform to experiment with such questions in a standardized way, rather than each research team building a custom hack for every project.
Challenges and Evolution: In terms of future development, MCP will need to tackle some challenges to realize these implications. Scalability is one – making sure MCP can function in distributed cloud environments as smoothly as on a local machine. Anthropic is working on remote server support, but this will involve robust authentication, encryption, and possibly brokered connections to allow enterprise-scale use. We can expect future versions of MCP to detail how an AI in the cloud might discover available servers, how to register new integrations in an organization, and how to handle concurrent multi-user scenarios (where one MCP server might serve many AI clients at once). Community governance is another aspect: as an open standard, MCP might eventually be stewarded by a broader consortium or foundation to encourage multi-party buy-in. If multiple AI providers contribute, the protocol could evolve to cover more use cases (for example, standards for image data exchange, or real-time streaming data into models). There’s also a possibility of competition – if MCP doesn’t gain critical mass, others might propose alternative protocols. However, the existence of MCP might also influence those alternatives, potentially steering the whole industry toward some common ground on how AI-to-tool communication should work. In any case, the very presence of MCP in the landscape has made the concept of LLM integration protocols mainstream, so future AI platforms will likely need to address similar needs even if named differently.

Resources and additional reading:

Anthropic, “Introducing the Model Context Protocol”
InfoQ (A. Alford), “Anthropic Publishes Model Context Protocol Specification for LLM App Integration,” Dec. 24, 2024
Runloop AI (A. Wall), “MCP – Understanding the Game-Changer,” Jan. 26, 2025
SalesforceDevOps (V. Keenan), “Anthropic’s MCP: ‘ODBC for AI’ in an Accelerating Market,” Nov. 29, 2024
Simon Willison’s Weblog, “Introducing the Model Context Protocol,” Nov. 25, 2024
Stackademic (H. Fernando), “MCP Simplified,” Mar. 2025
Docker Blog (J. Clark & D. S. Parra), “Simplifying AI apps with MCP and Docker,” Dec. 2024