Model Context Protocol (MCP)

Model Context Protocol (MCP) is used as a standardized integration layer that enables language models to interact reliably with external tools, services, and data sources.

It provides a clear contract between LLMs and the systems they operate on, helping move AI applications from ad-hoc tool calls to structured, extensible architectures.

Key Capabilities

• Standardized Tool Interfaces
  Defines consistent, well-structured contracts for invoking external tools from LLMs.

• Typed Inputs & Outputs
  Enforces structured schemas, reducing ambiguity and improving reliability of tool interactions.

• Context Propagation
  Maintains and passes relevant context across model–tool interactions.

• Extensible Integration Model
  Allows new tools and services to be added without changing core application logic.

• Separation of Concerns
  Decouples model reasoning from execution logic and system integrations.

Experience & Platform Contribution

Applied MCP to design tool-integrated LLM workflows that required reliable interaction with external systems and data sources.

Key contributions included:

• Integrating LLMs with domain-specific databases and analytical tools
• Defining clear tool schemas and interaction contracts
• Enabling structured data retrieval and processing through LLM-driven workflows
• Reducing brittleness and prompt-level coupling in tool usage
• Advising on protocol-based approaches for scalable AI system integration

MCP served as a foundational building block for building maintainable, extensible AI systems, where language models operate within well-defined system boundaries rather than relying on implicit behavior.