Model Context Protocol (MCP)

The Model Context Protocol (MCP) forces a separation between the definition of a tool (the server) and the consumption of a tool (the client/agent). Instead of hardcoding API integrations inside your agent's codebase, you build an MCP Server that exposes resources (data) and tools (functions). Any MCP-compliant agent can then discover and use these tools without custom glue code.

When to Use

• Standardization: When building an ecosystem of tools that many different agents need to use.

• Security: To expose internal data safely without giving the LLM direct database access.

• Modularity: To keep your agent logic clean and focused on reasoning, while the MCP server handles the "dirty work" of API connections.

• Portability: Tools built with MCP can be used by Claude Desktop, IDEs, and custom agents alike.

Use Cases

• Database Access: An MCP server that exposes safe SQL queries as tools.

• File System: An MCP server that allows an agent to read/write files in a sandboxed directory.

• API Wrapper: An MCP server that wraps the GitHub API, exposing actions like create_issue or list_prs .

Implementation Pattern

MCP Server Implementation (Conceptual)

from fastmcp import FastMCP, tool

Create a server

mcp = FastMCP("MyTools")

Expose a tool

@mcp.tool() def calculate_vat(amount: float, country: str) -> float: """Calculates VAT for a given country.""" rate = get_rate(country) return amount * rate

The Agent (Client) simply connects to this server

and automatically "sees" the calculate_vat tool available for use.

client.connect(mcp_server)

response = client.chat("How much VAT for 100 EUR in Germany?")