Anthropic's Model Context Protocol - Why enterprises should pay attention.
We have already seen what LLMs are capable of. Now imagine, if LLMs could break out of their confines and fetch data and move between tools while maintaining context.
Pretty excited with the possibilities that Model Context Protocol opens up in leveraging LLMs in an enterprise context. Until now there were limited ways to extend the out of the box functionality provided by LLMs which is (in most cases) a black box trained on general data with a specific cut off date. However for an LLM to thrive in the enterprise, it needs to seamlessly integrate with the vast ecosystem of enterprise data and also extend its reach to the various internal and external systems via API. That's the promise of the Model Context Protocol (MCP).
The promise of MCP is that of a simple reliable way for LLMs to get the information they need on demand using a universal, open standard for connecting AI systems with the data sources, replacing fragmented integrations with a single protocol.
From an enterprise context, this is exactly what you want to see. The open standard allows for reduced integration complexity, accelerates enterprise adoption with its reusable architectural components, enables more context aware AI interactions based on real data and not hallucinations. The open source nature brings in the much needed transparency while dealing with sensitive data and LLMs.
Some of the first integrations that I personally tried out are with real time web search (Brave Search API) and with sqlite. Integrations with systems like Google Drive, Slack, GitHub, Git, Postgres, and Puppeteer are already available and open specification lets you build the integrations needed for your enterprise. Looking at the current traction, the ecosystem will explode with support for connectors in a short while.
If LLMs could break out of their digital blackbox and proactively fetch data, maintain context as they move between different tools and datasets, what would you build with it ?
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