Artificial Intelligence has evolved beyond simple chatbots into Agentic AI—systems where models can reason, plan, and take action by using external tools and APIs. The potential is huge, but adoption is slowed by real-world challenges, especially for enterprise applications. This is where Model Context Protocol (MCP) can make a big difference.
Challenges of Agentic AI
- Fragmented Tooling & Ecosystems: Every major framework—LangChain, AutoGen, Semantic Kernel, CrewAI—has its own way of connecting tools and APIs. This creates “vendor lock-in” and poor reusability. A tool built for LangChain usually won’t work in AutoGen.
- Context Sharing Issues: Agents need memory, history, and access to external knowledge to work effectively. But today, context sharing is inconsistent and unreliable. Agents often “forget” important details, leading to errors and inefficiency.
- Complex Integrations: Connecting agents to databases, APIs, or enterprise apps often requires custom-built connectors. This makes integrations slow, expensive, and error-prone.
- Scalability & Coordination: When multiple agents are involved, it’s unclear who should talk to whom, what state should be shared, and how permissions should be managed. Without standards, multi-agent systems quickly become fragile.
- Security & Governance Gaps: There’s no unified way to define what tools an agent can use, what data it can access, or under what conditions. For enterprises, this creates serious risks around privacy, security, and compliance.
What is MCP?
Model Context Protocol (MCP) is an open-source standard created by Anthropic to connect large language models (LLMs) with external tools and data sources. MCP standardizes how agents discover, understand, and use tools—reducing the need for custom integrations and enabling more powerful and reliable AI assistants.
How MCP Fixes These Problems:
- Standardized Tool & Model Interface: MCP defines a universal interface for connecting models, tools, and data. Build a tool once, and it works across multiple frameworks.
- Shared Context Layer: MCP allows context—user inputs, memory, documents, system state—to be passed in a structured, consistent way, so agents don’t lose critical information.
- Plug-and-Play Integration: MCP works in plug-and-play type. Add a new tool or API, and any MCP-compatible framework can use it instantly—no need to rewrite connectors.
- Cross-Framework Interoperability: By standardizing interactions, MCP makes tools future-proof and reusable across ecosystems like LangChain, AutoGen, and Semantic Kernel.
- Built-in Security & Permissions: MCP includes governance features so agents can only access approved tools and data, with full control and auditability for enterprises.
Conclusion
Agentic AI has massive potential to solve real-world problems. MCP addresses the challenges by creating a universal protocol for agent-to-tool communication—similar to how HTTP standardized the web. The future of Agentic AI how well different systems work together. MCP is leading that shift.