How did the MCP change the process of tool calling in AI Agents?

I collected 6+ reasons to figure out how they did it...

Since the beginning, MCP came up with a unique solution of utilizing tools within LLM agents.

It is not just about simply connecting LLMs with different tools using function calling, it was providing a unified interface for tools.

Not only was tool calling simplified, but MCP also brought a huge change in the entire market.

📌 Let me share some of those with you:

1. Standardized Integration

• MCP standardizes AI agent connections to external tools and data sources, simplifying integration and reducing custom implementations.

2. Enhanced Context Awareness:

• By integrating MCP, AI agents can better retrieve relevant information, understand the context around tasks, and produce more nuanced and functional outputs with fewer attempts.

3. Scalability and Efficiency

• MCP enables Agents to maintain context as they move between different tools and datasets, replacing fragmented integrations with a more sustainable architecture.

4. Dynamic Tool Discovery

• MCP servers can dynamically discover and adapt to available tools, making it easier for AI agents to access and utilize the most relevant resources without manual configuration.

5. Interoperability

• MCP acts as a universal interface, similar to USB-C for LLM Agents, enabling seamless and secure data exchange between AI models and external resources.

6. Reduced Maintenance

• With MCP, developers can reduce AI agent maintenance time, as the protocol automatically updates actions and knowledge as functionality evolves.

7. Open-Source Ecosystem:

• The open-source nature of MCP has encouraged massive collaboration, as the community server list has already surpassed 300 servers.

📌 However, though MCP is very powerful, it still has some notable issues:

1. Current MCP implementations lack standard authentication mechanisms for client-server interactions.

2. There is a need for fine-grained permissions and consistent security across tool interactions.

3. Finding relevant tools across a large pool is difficult, even with MCP.

4. MCP lacks a built-in workflow concept to manage multi-step processes.

Though these problems currently exist, the team has shared their concern and will be working towards the solution.

For more detailed info, check the sources in the comments.

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Author

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