What is the Model Context Protocol (MCP)?

Overview

The Model Context Protocol (MCP) is an open standard that defines how Large Language Models (LLMs) can securely and intelligently interact with external systems, structured data, and operational tools. Its purpose is to provide a consistent, schema-based communication framework that enables AI assistants to perform actions such as reading files, calling APIs, analyzing logs, or executing custom tools — all while maintaining strict control, discoverability, and predictability in how these interactions occur.

Core Functionality

At its core, MCP creates a clean, structured interface between an AI assistant and the environment in which it operates. By abstracting the complexity of tool and data integration behind a protocol layer, MCP allows AI systems to request and utilize external capabilities without hardcoding dependencies or exposing sensitive internal mechanisms. This makes MCP a foundational enabler for operational AI — moving beyond conversational outputs into actionable, context-driven workflows.

Client-Server Architecture

MCP follows a client-server architecture in which the host (for example, Feluda.ai or Claude Desktop) runs an MCP client that can connect to one or more MCP servers. Each server is capable of exposing three main categories of capabilities:

• Resources: Structured data objects such as logs, documents, or APIs, described with metadata like URI, MIME type, and size.
• Tools: Executable functions such as search, fetch, or transform operations, each defined with a complete JSON Schema for input validation.
• Prompts: Predefined LLM workflows that encapsulate reusable, parameterized instructions and may include attached contextual resources.

Communication between the client and server occurs over a transport layer such as stdio or HTTP, using the JSON-RPC 2.0 protocol to ensure standardized, structured message exchange.

Typed, Discoverable, and Composable

One of MCP’s key strengths is that all exposed capabilities are typed, ensuring strict adherence to input and output expectations; discoverable, allowing AI agents to query available tools and resources at runtime; and composable, enabling different MCP servers and capabilities to be combined dynamically to perform complex tasks. This structure allows models to reason over capabilities rather than relying solely on free-text interpretation, making them more reliable and efficient in tool selection and workflow orchestration.

Deployment Flexibility

MCP is language-agnostic and transport-agnostic, meaning it can be implemented in any programming language and deployed across both local and remote environments. Its flexibility makes it suitable for a wide range of scenarios, from on-device AI assistants to enterprise-scale cloud integrations. The protocol supports advanced features including real-time updates, streaming responses, human-in-the-loop approvals, subscriptions for continuous data monitoring, and progress tracking for long-running operations.

MCP in Practice

In Feluda.ai’s modular assistant architecture, MCP acts as the connective tissue between AI agents and the system’s vast ecosystem of tools, resources, and workflows. By exposing these capabilities through MCP, Feluda assistants can securely discover, access, and orchestrate operational functions without bypassing security constraints or requiring direct integration into the model’s internal logic. This ensures that even advanced AI-driven actions remain controlled, auditable, and reproducible.

Strategic Significance

The Model Context Protocol transforms LLMs from passive conversational systems into real software agents capable of acting within defined boundaries, remembering relevant context, and collaborating across interconnected systems. By separating the model’s reasoning from its execution environment, MCP enables a future where AI can seamlessly integrate with diverse infrastructures while preserving trust, security, and operational control.

Conclusion

In essence, MCP is the invisible yet indispensable protocol layer that bridges AI reasoning with real-world action. It empowers AI assistants to see, act, remember, and collaborate — not merely respond — and establishes the technical foundation for scalable, secure, and composable AI-powered operations.