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Model Context Protocol

Developed by	Anthropic
Introduced	November 25, 2024 (2025-08-05)
Website	modelcontextprotocol.io

Relationship between MCP client and server

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The Model Context Protocol (MCP) is an open standard, open-source framework introduced by Anthropic in November 2024 to standardize the way artificial intelligence (AI) systems like large language models (LLMs) integrate and share data with external tools, systems, and data sources. MCP provides a universal interface for reading files, executing functions, and handling contextual prompts. Following its announcement, the protocol was adopted by major AI providers, including OpenAI and Google DeepMind.

Background

The protocol was announced by Anthropic in November 2024 as an open standard for connecting AI assistants to data systems such as content repositories, business management tools, and development environments. It aims to address the challenge of information silos and legacy 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" data integration problem.

Earlier stop-gap approaches - such as OpenAI’s 2023 “function-calling” API and the ChatGPT plug-in framework - solved similar problems but required vendor-specific connectors. MCP’s authors note that the protocol deliberately re-uses the message-flow ideas of the Language Server Protocol (LSP) and is transported over JSON-RPC 2.0.

Features

MCP defines a standardized framework for integrating AI systems with external data sources and tools. It includes specifications for data ingestion and transformation, contextual metadata tagging, and AI interoperability across different platforms. The protocol also supports secure, bidirectional connections between data sources and AI-powered tools.

MCP enables developers to expose their data via MCP servers or to develop AI applications—referred to as MCP clients—that connect to these servers. Key components of the protocol include a formal protocol specification and software development kits (SDKs), local MCP server support in Claude Desktop applications, and an open-source repository of MCP server implementations.

Applications

MCP has been applied in domains such as software development, business process automation, and natural language automation.

One prominent use case is in desktop assistants, where applications such as the Claude Desktop app deploy local MCP servers to enable secure access to system tools and user files. In enterprise settings, internal assistants are enhanced with MCP to retrieve data from proprietary documents, CRM systems, and internal knowledge bases—companies like Block have integrated MCP into their internal tooling for this purpose.

MCP also plays a critical role in multi-tool agent workflows, allowing agentic AI systems to coordinate multiple tools—for example, combining document lookup with messaging APIs—to support advanced, chain-of-thought reasoning across distributed resources.

In the field of natural language data access, MCP enables applications such as AI2SQL to bridge language models with structured databases, allowing plain-language queries.

MCP has been adopted for academic research workflows through integrations with reference management systems like Zotero. Multiple server implementations allow researchers to perform semantic searches across their libraries, extract PDF annotations, and generate literature reviews through AI-assisted analysis.

The protocol has become increasingly common in software development tools. Integrated development environments (IDEs) like Zed, coding platforms such as Replit, and code intelligence tools like Sourcegraph have adopted MCP to grant AI coding assistants real-time access to project context. This integration is especially valuable for workflows like "vibe coding", where continuous, adaptive assistance is essential.

In web application development, companies like Wix have embedded MCP servers into their platforms. This allows AI tools to interact with live website data, enabling dynamic content generation and on-the-fly edits. Such capabilities are central to Wix’s AI-driven development tools.

Implementation

The protocol was released with software development kits (SDKs) in programming languages including Python, TypeScript, C# and Java. Anthropic maintains an open-source repository of reference MCP server implementations for popular enterprise systems including Google Drive, Slack, GitHub, Git, Postgres, Puppeteer and Stripe. Developers can create custom MCP servers to connect proprietary systems or specialized data sources to AI systems.

The protocol's open standard allows organizations to build tailored connections while maintaining compatibility with the broader MCP ecosystem. AI systems can then leverage these custom connections to provide domain-specific assistance while respecting data access permissions.

Adoption

In March 2025, OpenAI officially adopted the MCP, following a decision to integrate the standard across its products, including the ChatGPT desktop app, OpenAI's Agents SDK, and the Responses API. Sam Altman described the adoption of MCP as a step toward standardizing AI tool connectivity. Prior to OpenAI's adoption, the potential benefits of MCP had been discussed extensively within the developer community, particularly for simplifying development in multi-model environments.

By adopting MCP, OpenAI joins other organizations such as Block, Replit, and Sourcegraph in incorporating the protocol into their platforms. This wide adoption highlights MCP's potential to become a universal open standard for AI system connectivity and interoperability. MCP can be integrated with Microsoft Semantic Kernel, and Azure OpenAI. MCP servers can be deployed to Cloudflare.

Demis Hassabis, CEO of Google DeepMind, confirmed in April 2025 MCP support in the upcoming Gemini models and related infrastructure, describing the protocol as "rapidly becoming an open standard for the AI agentic era".

Microsoft has made significant investments in the MCP to enhance AI integration across its ecosystem, including GitHub, Microsoft 365, and Azure. GitHub, alongside Microsoft, joined the MCP steering committee at Microsoft Build 2025, contributing a registry service for MCP server discovery and management. The Azure MCP Server, in public preview, connects AI agents to Azure services like storage, databases, and log analytics, while Microsoft 365 supports MCP for building AI agents and applications with Copilot integration. In May 2025, Microsoft released native MCP support in Copilot Studio, offering one-click links to any MCP server, new tool listings, streaming transport, and full tracing and analytics. The release positioned MCP as Copilot’s default bridge to external knowledge bases, APIs, and Dataverse. Additionally, Microsoft partnered with Anthropic to develop an official C# SDK for MCP, available as an open-source NuGet package (ModelContextProtocol), enabling seamless AI integration within the .NET ecosystem for building MCP servers and clients.

Many MCP servers have since been added, allowing integration of LLMs with diverse applications.

Reception

The Verge reported that MCP addresses a growing demand for AI agents that are contextually aware and capable of securely pulling from diverse sources. The protocol's rapid uptake by OpenAI, Google DeepMind, and toolmakers like Zed and Sourcegraph suggests growing consensus around its utility.

In April 2025, security researchers released analysis that there are multiple outstanding security issues with MCP, including prompt injection, tool permissions where combining tools can exfiltrate files, and lookalike tools can silently replace trusted ones.

It has been likened to OpenAPI, a similar specification that aims to describe APIs.

See also

• AI governance – Guidelines and laws to regulate AIPages displaying short descriptions of redirect targets
• Application programming interface – Connection between computers or programsPages displaying short descriptions of redirect targets
• LangChain – Language model application development framework
• Machine learning – Study of algorithms that improve automatically through experience
• Software agent – Computer program acting for a user

Further reading

• Hou, Xinyi; Zhao, Yanjie; Wang, Shenao; Wang, Haoyu (2025). "Model Context Protocol (MCP): Landscape, Security Threats, and Future Research Directions". arXiv:2503.23278 [cs.CR].
• Edwards, Benj (April 1, 2025). "MCP: The new "USB-C for AI" that's bringing fierce rivals together". Ars Technica.
• Jackson, Fiona (March 28, 2025). "OpenAI Agents Now Support Rival Anthropic's Protocol, Making Data Access 'Simpler, More Reliable'". TechRepublic.
• Masson, Colin (March 25, 2025). "Context Is the Missing Link: The Emergence of the Model Context Protocol in Industrial AI". ARC Advisory Group.
• Jimin Kim; Anita Lewis; Justin Lewis; Laith Al-Saadoon; Paul Vincent; Pranjali Bhandari (April 1, 2025). "Introducing AWS MCP Servers for code assistants (Part 1)". Amazon AWS.
• Desai, Zankar (March 19, 2025). "Introducing Model Context Protocol (MCP) in Copilot Studio: Simplified Integration with AI Apps and Agents". Microsoft Copilot Studio Blog, Microsoft.
• Wagner, Tim (May 13, 2025). "Understanding Model Context Protocol (MCP)". Vendia.

External links

• Official website
• modelcontextprotocol on GitHub
• SDK documentation from OpenAI