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Model Context Protocol (MCP)

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Content provided by Arize AI. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Arize AI or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ppacc.player.fm/legal.

We cover Anthropic’s groundbreaking Model Context Protocol (MCP). Though it was released in November 2024, we've been seeing a lot of hype around it lately, and thought it was well worth digging into.

Learn how this open standard is revolutionizing AI by enabling seamless integration between LLMs and external data sources, fundamentally transforming them into capable, context-aware agents. We explore the key benefits of MCP, including enhanced context retention across interactions, improved interoperability for agentic workflows, and the development of more capable AI agents that can execute complex tasks in real-world environments.

Learn more about AI observability and evaluation, join the Arize AI Slack community or get the latest on LinkedIn and X.

  continue reading

47 episodes

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Model Context Protocol (MCP)

Deep Papers

22 subscribers

published

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Manage episode 473322113 series 3448051
Content provided by Arize AI. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Arize AI or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ppacc.player.fm/legal.

We cover Anthropic’s groundbreaking Model Context Protocol (MCP). Though it was released in November 2024, we've been seeing a lot of hype around it lately, and thought it was well worth digging into.

Learn how this open standard is revolutionizing AI by enabling seamless integration between LLMs and external data sources, fundamentally transforming them into capable, context-aware agents. We explore the key benefits of MCP, including enhanced context retention across interactions, improved interoperability for agentic workflows, and the development of more capable AI agents that can execute complex tasks in real-world environments.

Learn more about AI observability and evaluation, join the Arize AI Slack community or get the latest on LinkedIn and X.

  continue reading

47 episodes

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