Model Context Protocol
Protocol for communicating between LLMs and applications From Wikipedia, the free encyclopedia
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][3]
 | This article incorporates text from a large language model. (April 2025) |
| Developed by | Anthropic |
|---|---|
| Introduced | November 25, 2024 |
| Website | modelcontextprotocol |
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#.[4]
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.[5] There are many MCP servers today, allowing integration of LLMs with different applications.[6]
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.[5]
- 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.
- Desktop assistants: The Claude Desktop app runs local MCP servers to allow the assistant to read files or interact with system tools securely.[4]
- 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.
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,[when?] dozens of MCP server implementations had been released, including community-maintained connectors for Slack, GitHub, PostgreSQL, Google Drive, and Stripe.[8]
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] Developers praised its plug-and-play architecture and model-agnostic design on platforms like GitHub and Hacker News.[8]
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
External links
Wikiwand - on
Seamless Wikipedia browsing. On steroids.