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Model Context Protocol
Protocol for communicating between LLMs and applications From Wikipedia, the free encyclopedia
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The Model Context Protocol (MCP) is an open standard, open-source framework introduced by Anthropic to standardize the way artificial intelligence (AI) models like large language models (LLMs) integrate and share data with external tools, systems, and data sources.[1] Designed to standardize context exchange between AI assistants and software environments, MCP provides a model-agnostic universal interface for reading files, executing functions, and handling contextual prompts.[2] It was officially announced and open-sourced by Anthropic in November 2024, with subsequent adoption by major AI providers including OpenAI and Google DeepMind.[3][4]
 | This article incorporates text from a large language model. (April 2025) |
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Background
The protocol was announced in November 2024 as an open standard[5] for connecting AI assistants to data systems such as content repositories, business management tools, and development environments.[6] It addresses the challenge of information silos and legacy systems that constrain even the most sophisticated AI models.[6]
Anthropic 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" data integration problem.[6]
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 software development kits (SDK) in multiple programming languages, including Python, TypeScript, Java, and C#.[7]
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Features
MCP defines a set of specifications for:
- Data ingestion and transformation
- Contextual metadata tagging
- Model interoperability across platforms
- Secure, two-way connections between data sources and AI-powered tools[6]
The protocol enables developers to either expose their data through MCP servers or build AI applications (MCP clients) that connect to these servers.[6] Key components include:
- Protocol specification and SDKs
- Local MCP server support in Claude Desktop apps
- Open-source repository of MCP servers[6]
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Applications
MCP has been applied across a range of use cases in software development, business process automation, and natural language automation:
- Software development: Integrated development environments (IDE) such as Zed, platforms like Replit, and code intelligence tools such as Sourcegraph integrated Model Context Protocols (MCP) to give coding assistants access to real-time code context, useful in vibe coding.[5]
- Enterprise assistants: Companies like Block use MCP to allow internal assistants to retrieve information from proprietary documents, customer relationship management (CRM) systems, and company knowledge bases.[6]
- Natural language data access: Applications like AI2SQL leverage MCP to connect models with SQL databases, enabling plain-language information retrieval.
- Desktop assistants: The Claude Desktop app runs local MCP servers to allow the assistant to read files or interact with system tools securely.[7]
- 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.
Implementation
Anthropic has provided pre-built MCP servers for popular enterprise systems including:
The open-source repository of MCP server implementations is available on GitHub, providing developers with examples and foundations for building custom integrations.[9]
Developers can create custom MCP servers to connect proprietary systems or specialized data sources to AI models. These custom implementations enable:
- Real-time access to private databases and internal tools
- Secure integration with sensitive business systems
- Context-aware AI responses based on organizational knowledge
- Automated workflows across multiple business applications
- Custom data processing pipelines for AI consumption
The protocol's open standard allows organizations to build tailored connections while maintaining compatibility with the broader MCP ecosystem. AI models can then leverage these custom connections to provide domain specific assistance while respecting data access permissions.[6]
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Adoption
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In March 2025, OpenAI officially adopted the Model Context Protocol (MCP), following a decision to integrate the standard across its products, including the ChatGPT desktop app. OpenAI CEO Sam Altman announced the move, emphasizing that MCP support would be available in the OpenAI Agents SDK, with future support planned for the ChatGPT desktop application and Responses API. This integration allows developers to connect their MCP servers to AI agents, simplifying the process of providing tools and context to large language models (LLMs).
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.[3][10]
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.[11] MCP can be integrated with Microsoft Semantic Kernel,[12] and Azure OpenAI.[13] MCP servers can be deployed to Cloudflare.[14]
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".[15]
Early adopters of MCP included Block and Sourcegraph, both of whom used the protocol to allow internal AI systems to access proprietary knowledge bases and developer tools.[5] There are many MCP servers today, allowing integration of LLMs with different applications.[16]
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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.[5] The protocol's rapid uptake by OpenAI, Google DeepMind, and toolmakers like Zed and Sourcegraph suggests growing consensus around its utility.[3][15]
In April 2025, security researchers released analysis that there are multiple outstanding security issues with MCP, including prompt injection,[17] tool permissions where combining tools can exfiltrate files,[18] and lookalike tools can silently replace trusted ones.[19]
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See also
- AI governance – Guidelines and laws to regulate AI
- Application programming interface – Connection between computers or programs
- LangChain – Language model application development framework
- Machine learning – Study of algorithms that improve automatically through experience
- Eclipse Theia – Open-source framework for building IDEs
- Software agent – Computer program acting for a user
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Notes
References
External links
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