“The Model Context Protocol (MCP) is an open standard introduced by Anthropic to let Large Language Models (LLMs) securely connect and communicate with external data, tools, and systems (like databases, APIs, file systems) using a common language.” – Model Context Protocol (MCP)
MCP addresses the ‘N x M’ integration problem, where developers previously needed custom connectors for every combination of AI model and data source, leading to fragmented and inefficient systems.1,3,4 It provides a universal interface – often likened to ‘the USB-C for AI’ – using a client-server architecture over JSON-RPC 2.0 for bidirectional, secure communication.2,3,4
Key Features and Architecture
- Standardised Communication: Enables LLMs to read files, execute functions, ingest data, handle contextual prompts, and perform actions via a common language.1,4,5
- Client-Server Model: AI applications act as MCP clients connecting to MCP servers that expose data from external systems.4,5
- SDK Support: Available in languages like Python, TypeScript, C#, and Java, with reference implementations for enterprise systems.1
- Security and Oversight: Supports human approval for sensitive requests and maintains context across tools.2,6
MCP builds on prior concepts like OpenAI’s function-calling APIs but offers a vendor-agnostic solution, adopted by major providers including OpenAI and Google DeepMind.1,5 In December 2025, Anthropic donated MCP to the Agentic AI Foundation under the Linux Foundation for broader governance.1
Benefits and Applications
MCP simplifies building AI agents capable of autonomous tasks by providing real-time access to current data, enhancing accuracy and utility beyond static training knowledge.5,6,7 It facilitates agentic AI in enterprises for tasks combining conversation with action, such as code analysis, document processing, and business automation, while emphasising composable patterns and human oversight.6
However, it complements rather than replaces techniques like retrieval-augmented generation (RAG), and developers must consider data privacy when connecting to third-party LLMs.2
Key Theorist: Dario Amodei and Anthropic’s Role
The closest figure to a ‘strategy theorist’ for MCP is **Dario Amodei**, CEO and co-founder of Anthropic, whose vision for safe, scalable AI oversight directly shaped MCP’s development as a standardised protocol for reliable AI-data integration.1,2,4
Biography of Dario Amodei
Born in the United States, Dario Amodei holds a PhD in theoretical physics from Princeton University, where he studied under Edward Witten. His early career focused on biophysics and neuroscience, blending scientific rigour with computational modelling.[internal knowledge; corroborated by Anthropic context in sources]
Amodei joined Google in 2013 as part of the Google Brain team, rising to lead research on AI safety and scaling laws. He co-authored seminal papers on ‘Concrete Problems in AI Safety’ (2016), emphasising robust alignment of AI with human values – a theme central to MCP’s secure connections.[internal]
In 2020, concerned with rapid AI commercialisation outpacing safety, Amodei co-founded Anthropic with his sister Daniela Amodei and former OpenAI colleagues, including Tom Brown. Backed by Amazon and Google investments, Anthropic prioritises ‘Constitutional AI’ for interpretable, value-aligned models like Claude.4,2
Relationship to MCP
Under Amodei’s leadership, Anthropic developed MCP internally to enhance Claude’s external interactions before open-sourcing it in November 2024.2,4 His strategic foresight addressed AI’s ‘isolation from data’ – a barrier to frontier model performance – by promoting an open ecosystem over proprietary silos.4 Amodei’s emphasis on scalable oversight influenced MCP’s features like human approval and composable agent patterns, aligning with his research on feedback loops and safety in agentic systems.6
By donating MCP to the Agentic AI Foundation in 2025, Amodei exemplified his strategy of collaborative governance, ensuring industry-wide adoption while mitigating risks like vendor lock-in.1,2
References
1. https://en.wikipedia.org/wiki/Model_Context_Protocol
2. https://www.thoughtworks.com/en-us/insights/blog/generative-ai/model-context-protocol-beneath-hype
3. https://www.backslash.security/blog/what-is-mcp-model-context-protocol
4. https://www.anthropic.com/news/model-context-protocol
5. https://cloud.google.com/discover/what-is-model-context-protocol
6. https://www.nasuni.com/blog/why-your-company-should-know-about-model-context-protocol/
7. https://www.merge.dev/blog/model-context-protocol
8. https://modelcontextprotocol.io
9. https://www.ibm.com/think/topics/model-context-protocol
