Скачать или смотреть What is Model Context Protocol (MCP) - The future of AI Agents

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ഈ വീഡിയോയിൽ ആന്ത്രോപിക് വികസിപ്പിച്ച MCP (Model Context Protocol) ലളിതമായി മനസ്സിലാക്കാം. M×N ഇന്റഗ്രേഷൻ പ്രശ്നം എങ്ങനെ M+N ആയി ലളിതമാക്കാം, Host/Client/Server ആർക്കിടെക്ചർ, Tools/Resources/Prompts എന്നീ പ്രിമിറ്റീവുകൾ എന്നിവ യാഥാർത്ഥ്യ ഉദാഹരണത്തോട് കൂടി വിശദീകരിക്കുന്നു.

Build smarter AI agents faster. In this Malayalam session, learn MCP (Model Context Protocol) by Anthropic (Claude) — the open standard that turns messy M×N integrations into clean M+N. Perfect for data science students, AI enthusiasts, and anyone planning a career in AI/LLM agents, tool-use, and workflow automation.

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What you’ll learn (high-value outcomes)

1. Understand the M×N integration problem and how MCP standardizes tool connectivity
2. MCP architecture: Host, Client, Server; and primitives: Tools, Resources, Prompts
3. Real-world example: Google Calendar scheduling with context fetch
3. How MCP skills map to portfolio projects, interviews, and industry needs


00:00–01:03 • Why MCP matters + video overview (Malayalam)
01:03–01:46 • Anthropic’s MCP: open standard (free to adopt)
01:46–03:21 • AI apps need external data/actions (Gmail, GitHub, Slack, DBs)
03:21–05:14 • USB‑C analogy: a universal connector for AI tool integrations
05:14–09:07 • The M×N integration problem (multiple models × multiple tools)
09:07–10:20 • MCP reduces M×N → M+N (≈55% complexity reduction)
10:20–12:18 • MCP Host and Client explained (user-facing app + 1:1 server links)
12:18–17:39 • MCP Server + primitives (Tools, Resources, Prompts)
17:39–21:23 • Example: Schedule a meeting with Google Calendar using MCP
21:23–24:00 • End-to-end communication flow (capability discovery → permission → action)
24:00–26:05 • Quick quiz + what’s next (code, n8n integrations) + course info

Why this matters for your AI/Data Science career ?

1. Faster prototyping: Connect LLMs to Gmail, GitHub, Slack, DBs without bespoke code per combo
2. Scalable systems: Standard interfaces make maintenance and multi-model strategies practical
3. Interview advantage: Explain M×N vs M+N clearly with concrete diagrams/examples
4. Portfolio-ready: Showcase agentic workflows that actually execute tasks via MCP

Who should watch ?

Data science students moving beyond notebooks into production AI
AI enthusiasts building agents, copilots, automations. Career switchers targeting AI engineer / LLM engineer / ML platform roles

Use this video for your portfolio (project ideas)

1. MCP Calendar Assistant: Auto-schedule meetings, fetch last notes, propose agenda
2. GitHub Issue Triage Agent: Label, assign, and create issues from natural language
3. Slack Standup Bot: Summarize updates, create follow-ups in Calendar/Drive via MCP

Tip: Document architecture (Host/Client/Server), capability catalog, and security/permissions.
Next steps

Upcoming parts: Code-level demos + n8n/other workflow integrations

Want structured learning, projects, and career coaching? Join our AI/Data Science programs (hands-on labs, capstones, interview prep, mentorship).

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