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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 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.[1] MCP provides a universal interface for reading files, executing functions, and handling contextual prompts.[2] Following its announcement, the protocol was adopted by major AI providers, including OpenAI and Google DeepMind.[3][4]
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Background
The protocol was announced by Anthropic 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 aims to address the challenge of information silos and legacy systems.[6] 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]
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.[7] 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.[8] MCP formally specifies stdio and HTTP (optionally with SSE) as its standard transport mechanisms.[9]
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Features
MCP defines a standardized framework for integrating AI systems with external data sources and tools.[2] 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.[6]
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.[6] 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.[6]
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Applications
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.[8]
The protocol is used in AI-assisted software development tools. Integrated development environments (IDEs), 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.[5]
Implementation
The protocol was released with software development kits (SDKs) in programming languages including Python, TypeScript, C# and Java.[8][10] 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.[11] Developers can create custom MCP servers to connect proprietary systems or specialized data sources to AI systems.[11]
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.[6]
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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.[3][2]
MCP can be integrated with Microsoft Semantic Kernel,[12] and Azure OpenAI.[13] MCP servers can be deployed to Cloudflare.[14]
Demis Hassabis, CEO of Google DeepMind, confirmed in April 2025 MCP support in the upcoming Gemini models and related infrastructure.[4]
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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,[16] tool permissions where combining tools can exfiltrate files,[17] and lookalike tools can silently replace trusted ones.[18]
It has been likened to OpenAPI, a similar specification that aims to describe APIs.[19][20]
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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
- Software agent – Computer program acting for a user
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References
Further reading
External links
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