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Model Context Protocol

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Model Context Protocol
Developed byAnthropic
WebsiteModel Context Protocol (MCP)

The Model Context Protocol (MCP) is an open standard developed by the artificial intelligence company Anthropic for enabling large language model (LLM) applications to interact with external tools, systems, and data sources. Designed to standardize context exchange between AI assistants and software environments, MCP provides a model-agnostic interface for reading files, executing functions, and handling contextual prompts. It was officially announced and open-sourced by Anthropic in November 2024, with subsequent adoption by major AI providers including OpenAI and Google DeepMind.[1][2]

Background

Anthropic, known for the development of the Claude family of language models, introduced MCP to address the growing complexity of integrating LLMs with third-party systems. Before MCP, developers often had to build custom connectors for each data source or tool, resulting in what Anthropic described as an "N×M" integration problem.[1]

MCP was designed as a response to this challenge, offering a universal protocol for interfacing any AI assistant with any structured tool or data layer. The protocol was released with SDKs in multiple languages, including Python, TypeScript, Java, and C#.[3]

Early adopters of MCP included Block (formerly Square), Apollo, and Sourcegraph, all of whom used the protocol to allow internal AI systems to access proprietary knowledge bases and developer tools.[4]

Architecture

MCP follows a modular client–server architecture that decouples AI assistants from backend services. A typical MCP deployment includes:

  • A host process, such as a desktop assistant or chatbot.
  • One or more MCP clients, lightweight intermediaries spawned by the host.
  • One or more MCP servers, each exposing data and tools to the AI through a standard schema.

MCP clients communicate with servers using a JSON-RPC interface over streams such as stdio (for local processes) or HTTP with server-sent events (for remote services).[3]

Each MCP server declares its functionality using three categories:

  • Resources — static or queryable datasets (e.g., files, emails, documents).
  • Tools — invokable functions or APIs (e.g., "create task", "fetch database row").
  • Prompts — context-aware text templates (e.g., “summarize this report”).

All communication is brokered by the host, which manages connection permissions, orchestrates execution, and ensures that each MCP client operates within its own sandboxed environment.[5]

Applications

MCP has been applied across a range of use cases in software development, enterprise environments, and natural language automation:

  • Software development: IDEs such as Zed, platforms like Replit, and code intelligence tools such as Sourcegraph integrated MCP to give coding assistants access to real-time code context.[4]
  • Enterprise assistants: Companies like Block and Apollo use MCP to allow internal assistants to retrieve information from proprietary documents, CRM systems, and company knowledge bases.[1]
  • Natural language data access: Applications like AI2SQL leverage MCP to connect models with SQL databases, enabling plain-language queries.[5]
  • Desktop assistants: The Claude Desktop app runs local MCP servers to allow the assistant to read files or interact with system tools securely.[3]
  • Multi-tool agents: MCP supports agentic AI workflows involving multiple tools (e.g., document lookup + messaging APIs), enabling chain-of-thought reasoning over distributed resources.[6]

Adoption

On March 26, 2025, OpenAI announced support for MCP across its Agents SDK and ChatGPT desktop applications. CEO Sam Altman stated that "People love MCP and we are excited to add support across our products."[2]

Two weeks later, Demis Hassabis, CEO of Google DeepMind, confirmed MCP support in the upcoming Gemini models and related infrastructure, describing the protocol as a "rapidly emerging open standard for agentic AI".[7]

By mid-2025, dozens of MCP server implementations had been released, including community-maintained connectors for Slack, GitHub, PostgreSQL, Google Drive, and Stripe.[8]

Comparison with Other Systems

MCP has been compared to:

  • OpenAI Function Calling: While function calling lets LLMs invoke user-defined functions, MCP offers a broader, model-agnostic infrastructure for tool discovery, access control, and streaming interactions.[9]
  • OpenAI Plugins and “Work with Apps”: These rely on curated partner integrations, whereas MCP supports decentralized, user-defined tool servers.
  • Google Bard Extensions: Limited to internal Google products. MCP allows arbitrary third-party integrations.
  • LangChain / LlamaIndex: While these libraries orchestrate tool-use workflows, MCP provides the underlying communication protocol they can build upon.

Reception

Initial reception from developers and analysts has been largely positive. Forbes called MCP a "significant step forward in AI integration", emphasizing its power in simplifying how models interact with structured data.[5]

The Verge reported that MCP addresses a growing demand for AI agents that are contextually aware and capable of securely pulling from diverse sources.[4] Developers praised its plug-and-play architecture and model-agnostic design on platforms like GitHub and Hacker News.[8]

Some experts have raised concerns about overlap with existing standards like OpenAPI or the risk of fragmentation if too many competing protocols emerge.[6]

Nonetheless, the protocol's rapid uptake by OpenAI, Google DeepMind, and toolmakers like Zed and Sourcegraph suggests growing consensus around its utility.[2][7]

See also

References

  1. ^ a b c "Introducing the Model Context Protocol". Anthropic. November 25, 2024.
  2. ^ a b c "OpenAI adopts rival Anthropic's standard for connecting AI models to data". TechCrunch. March 26, 2025.
  3. ^ a b c "Model Context Protocol (MCP)". Anthropic Docs.
  4. ^ a b c "Anthropic launches tool to connect AI systems directly to datasets". The Verge. November 25, 2024.
  5. ^ a b c "Why Anthropic's Model Context Protocol Is A Big Step In The Evolution Of AI Agents". Forbes. November 30, 2024.
  6. ^ a b "MCP: Hype or Game-Changer for AI Integrations?". EBI.AI. March 10, 2025.
  7. ^ a b "What is Model Context Protocol (MCP) Explained". Beebom. April 14, 2025.
  8. ^ a b "Model Context Protocol". GitHub.
  9. ^ "Function Calling". OpenAI Docs.
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