Using MCP to Augment Agentic Capabilities

A Brief intro to MCP and where it fits in AI eco system

1. Agents and Tools

• Agents

  • Software entities (like AI assistants) that can reason, decide, and act.

  • They have goals (e.g., book a ticket, answer a query) and use tools to achieve them.

  • Types:

    • Task agents → perform one-off tasks.

    • Conversational agents → interact with users over multiple turns.

    • Autonomous agents → plan and act continuously.

• Tools

  • External systems/services agents use to execute actions.

  • Examples: Database queries, API calls (e.g., weather, stock prices), file systems, or LLM-powered functions.

  • Key idea: Agents are smarter when they can call tools.

2. API

• Definition: Application Programming Interface – contract to interact with software systems.

• Relevance to Agents:

  • Agents call APIs as tools to fetch or mutate data.

  • Well-defined APIs = predictable tool behavior.

• Structure:

  • Endpoint (/weather/today)

  • Method (GET, POST)

  • Input/output (JSON schemas).

  Example

GET /api/weather?city=Delhi
→ { "temp": 32, "condition": "Sunny" }

3. Model Context Protocol (MCP)

• Definition: An emerging open standard to define how AI models interact with external tools and data sources.

• Purpose: Provide a consistent way for LLMs to “know” what context and capabilities are available.

• Analogy: Like USB for AI tools – once defined, any agent can plug in.

Key Features:

• Declares what tools exist (capabilities).

• Defines how to call them (schemas).

• Handles responses and error flows.

4. MCP Architecture

• Components:

  1. Agent – the reasoning engine (e.g., GPT model).

  2. MCP Server – exposes tools & data sources in a standardized format.

  3. MCP Client – resides with the agent, translates reasoning into tool invocations.

  4. Transport Layer – channel (HTTP, WebSocket, gRPC).

High-level view:

Agent → MCP Client → MCP Server → Tool → MCP Server → MCP Client → Agent

5. MCP Communication Flow

1. Discovery

   • Agent loads MCP schema → learns what tools exist.

2. Invocation

   • Agent decides: “I need weather info.”

   • Sends structured request via MCP Client.

3. Execution

   • MCP Server calls the actual API/tool.

4. Response

   • Tool result is wrapped in MCP response → returned to agent.

5. Reasoning loop

   • Agent integrates response, continues plan.

Simplified sequence:

6. MCP Orchestration

• Definition: Coordinating multiple tools, APIs, and workflows through MCP.

• Example:

  • Goal = “Book me a flight to Bangalore tomorrow.”

  • Orchestration steps:

    1. Call calendar API → check availability.

    2. Call flights API → fetch options.

    3. Call payments API → confirm booking.

• MCP Role:

  • Ensures smooth chaining of tools.

  • Provides consistent error handling and retries.

  • Decouples agent logic from tool implementation.