MCP

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?")

Examples

Input: "Connect my agent to a database MCP server."

// .claude/settings.json
{
  "mcpServers": {
    "postgres": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-postgres"],
      "env": { "DATABASE_URL": "postgresql://localhost/mydb" }
    }
  }
}

Agent can now call mcp__postgres__query to run SQL and mcp__postgres__schema to inspect tables.

Input: "Build a multi-MCP workflow: search the web, then save results to Notion."

Pattern: Phase 1 calls mcp__brave__search, collects results. Phase 2 calls mcp__notion__createPage with formatted output. Each phase validates data before proceeding.

Troubleshooting