"MCP gives all three context. MCP stands for Model Context Protocol and was developed and open-sourced by Anthropic to standardize integrations between AI and the tools and data sources that can provide critical information in ways that enable LLMs to understand and take action. Instead of every service building out an integration for every AI agent, MCP defines a protocol where any application can maintain a single MCP server implementation that exposes its functionality,"
"Giving Data Scientists, agents, and LLMs access to tools through Model Context Protocol is necessary to provide additional context to their work, but it is not sufficient. Say I am trying to build AI that the Sales team at my organization can use to target which of our accounts is most likely to churn. If a sales team member asked an LLM directly without context it would surely hallucinate, it would not have any information on my organization, its customers, or the accounts"
AI systems, data scientists, and AI agents often produce misleading or superficially accurate outputs when lacking proper context. Model Context Protocol (MCP) standardizes integrations between AI and external tools or data sources by defining authentication, data exchange, and function-calling expectations. MCP enables applications to expose functionality through a single server implementation that any AI system can connect to. Providing access to tools via MCP can improve model responses only if the MCP server supplies accurate, relevant organizational context. Noisy or duplicated data in source systems, such as CRM records, can still lead to hallucinations or incorrect recommendations.
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