Model Context Protocol
Protocol for communicating between LLMs and applications
From Wikipedia, the free encyclopedia
The Model Context Protocol (MCP) is an open standard and 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]
| |
| Developed by | Anthropic |
|---|---|
| Introduced | November 25, 2024 |
| Industry | Artificial intelligence |
| Connector type | |
| Website | modelcontextprotocol |
Background
MCP 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][non-primary source needed]
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 re-uses the message-flow ideas of the Language Server Protocol (LSP) and is transported over JSON-RPC 2.0.[8]
In December 2025, Anthropic donated the MCP to the Agentic AI Foundation (AAIF), a directed fund under the Linux Foundation, co-founded by Anthropic, Block and OpenAI, with support from other companies.[9]
Features
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 enterprise systems.[citation needed]
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 bidirectional connections between data sources and AI tools.[6][non-primary source needed]
MCP enables applications such as querying structured databases with plain language in the field of natural language data access.[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]
Adoption
In March 2025, OpenAI officially adopted the MCP, after having integrated the standard across its products, including the ChatGPT desktop app.[3][2]
MCP can be integrated with Microsoft Semantic Kernel,[11] and Azure OpenAI.[12] MCP servers can be deployed to Cloudflare.[13][non-primary source needed]
Reception
The Verge reported that MCP addresses a growing demand for AI agents that are contextually aware and capable of 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][14]
In April 2025, security researchers released an analysis that concluded there are multiple outstanding security issues with MCP, including prompt injection,[15] tool permissions that allow for combining tools to exfiltrate data,[16] and lookalike tools that can silently replace trusted ones.[17]
MCP has been likened to OpenAPI, a similar specification that aims to describe APIs.[18][19]
See also
- Agent2Agent – Open protocol for communication between AI agents
- 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