mcp-architecture-expert

name	MCP Architecture Expert
description	Design and implement Model Context Protocol servers for standardized AI-to-data integration with resources, tools, prompts, and security best practices
version	1.1.0
last_updated	"2026-01-06T00:00:00.000Z"
external_version	MCP Spec 1.0, Oracle MCP Servers

MCP Architecture Expert Skill

Purpose

Master the Model Context Protocol (MCP) to build standardized, reusable integrations between AI systems and data sources, eliminating the N×M integration problem.

What is MCP?

Model Context Protocol

Open standard (November 2024, Anthropic) for connecting AI systems to external data sources and tools through a unified protocol.

The Problem: N agents × M tools = N×M custom integrations The Solution: N agents + M MCP servers = N+M integrations (any agent uses any tool)

Architecture

┌─────────────┐
│  MCP Host   │  (Claude Desktop, IDEs, Apps)
│   ┌─────┐   │
│   │Client│──┼──┐
│   └─────┘   │  │
└─────────────┘  │
                 │ JSON-RPC 2.0
                 │
┌────────────────┼─────────────┐
│  MCP Server    ▼             │
│  ┌──────────────────┐        │
│  │  Resources       │        │
│  │  Tools           │        │
│  │  Prompts         │        │
│  └──────────────────┘        │
│         │                    │
│         ▼                    │
│  ┌──────────────────┐        │
│  │ Data Source      │        │
│  │ (DB, API, Files) │        │
│  └──────────────────┘        │
└─────────────────────────────┘

Three Core Capabilities

1. Resources

Purpose: Expose data for AI to read

Examples:

• File contents
• Database records
• API responses
• Documentation

Definition:

{
  "resources": [
    {
      "uri": "file:///docs/api-spec.md",
      "name": "API Specification",
      "mimeType": "text/markdown"
    },
    {
      "uri": "db://customers/12345",
      "name": "Customer Record",
      "mimeType": "application/json"
    }
  ]
}

2. Tools

Purpose: Functions AI can invoke

Examples:

• Query database
• Call external API
• Process files
• Execute commands

Definition:

{
  "tools": [
    {
      "name": "query_database",
      "description": "Execute SQL query on customer database",
      "inputSchema": {
        "type": "object",
        "properties": {
          "query": {"type": "string"}
        },
        "required": ["query"]
      }
    }
  ]
}

3. Prompts

Purpose: Reusable prompt templates

Examples:

• Common task patterns
• Domain-specific workflows
• Best practice templates

Definition:

{
  "prompts": [
    {
      "name": "analyze_customer",
      "description": "Analyze customer behavior and generate insights",
      "arguments": [
        {
          "name": "customer_id",
          "description": "Customer identifier",
          "required": true
        }
      ]
    }
  ]
}

Building MCP Servers

Python Server Example

from mcp import Server, Tool, Resource

server = Server("customer-data")

@server.resource("customer://")
async def get_customer(uri: str):
    """Expose customer data as resources"""
    customer_id = uri.split("://")[1]
    return {
        "uri": uri,
        "mimeType": "application/json",
        "text": json.dumps(get_customer_data(customer_id))
    }

@server.tool()
async def query_customers(
    filters: dict
) -> list:
    """Query customer database"""
    return database.query("customers", filters)

@server.prompt()
async def customer_analysis(customer_id: str):
    """Generate customer analysis prompt"""
    return {
        "messages": [
            {
                "role": "user",
                "content": f"Analyze customer {customer_id} behavior and provide insights"
            }
        ]
    }

if __name__ == "__main__":
    server.run()

TypeScript Server Example

import { Server } from "@modelcontextprotocol/sdk/server/index.js";
import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js";

const server = new Server({
  name: "github-server",
  version: "1.0.0"
}, {
  capabilities: {
    resources: {},
    tools: {},
    prompts: {}
  }
});

server.setRequestHandler(ListResourcesRequestSchema, async () => {
  return {
    resources: [
      {
        uri: "github://issues",
        name: "GitHub Issues",
        mimeType: "application/json"
      }
    ]
  };
});

server.setRequestHandler(ListToolsRequestSchema, async () => {
  return {
    tools: [
      {
        name: "create_issue",
        description: "Create a new GitHub issue",
        inputSchema: {
          type: "object",
          properties: {
            title: { type: "string" },
            body: { type: "string" }
          }
        }
      }
    ]
  };
});

const transport = new StdioServerTransport();
await server.connect(transport);

Common MCP Servers

Official Servers (by Anthropic)

• GitHub - Issues, PRs, repos
• Slack - Messages, channels
• Google Drive - Files, docs
• PostgreSQL - Database queries
• Puppeteer - Web scraping
• Git - Repository operations
• Stripe - Payment data

Installing Official Servers

# Via npm
npx @modelcontextprotocol/server-github

# Via Docker
docker run mcp-postgres-server

# Via Python
pip install mcp-server-slack
python -m mcp_server_slack

Client Integration

Claude Desktop Configuration

{
  "mcpServers": {
    "github": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-github"],
      "env": {
        "GITHUB_TOKEN": "your-token"
      }
    },
    "postgres": {
      "command": "docker",
      "args": ["run", "mcp-postgres-server"],
      "env": {
        "DATABASE_URL": "postgresql://..."
      }
    }
  }
}

Claude SDK Integration

from anthropic import Anthropic

client = Anthropic()

response = client.messages.create(
    model="claude-sonnet-4-5",
    mcp_servers={
        "github": {
            "command": "npx",
            "args": ["-y", "@modelcontextprotocol/server-github"]
        }
    },
    messages=[{
        "role": "user",
        "content": "List my GitHub issues"
    }]
)

Security Best Practices

Authentication

# OAuth 2.0 with Resource Indicators (RFC 8707)
server = Server(
    "secure-api",
    auth_type="oauth2",
    scopes=["read:data", "write:data"]
)

@server.tool(required_scope="write:data")
async def update_record(record_id: str, data: dict):
    # Only callable with write permissions
    pass

Input Validation

@server.tool()
async def execute_query(query: str):
    # Validate to prevent injection
    if not is_safe_query(query):
        raise ValueError("Unsafe query detected")

    # Sanitize inputs
    safe_query = sanitize_sql(query)
    return database.execute(safe_query)

Rate Limiting

from functools import lru_cache
from time import time

@server.tool()
@rate_limit(calls=10, period=60)  # 10 calls per minute
async def expensive_operation():
    pass

Audit Logging

@server.tool()
async def sensitive_operation(data: dict):
    audit_log.write({
        "timestamp": datetime.now(),
        "operation": "sensitive_operation",
        "user": current_user(),
        "data": data
    })
    return process(data)

Advanced Patterns

Multi-Source Aggregation

@server.resource("aggregated://customer")
async def aggregate_customer_data(customer_id: str):
    """Combine data from multiple sources"""
    crm_data = await crm_server.get_resource(f"crm://{customer_id}")
    support_data = await support_server.get_resource(f"support://{customer_id}")
    analytics_data = await analytics_server.get_resource(f"analytics://{customer_id}")

    return {
        "uri": f"aggregated://customer/{customer_id}",
        "data": {
            **crm_data,
            **support_data,
            **analytics_data
        }
    }

Caching Layer

from functools import lru_cache

@server.resource("cached://")
@lru_cache(maxsize=1000)
async def cached_resource(uri: str):
    """Cache frequently accessed resources"""
    return await expensive_fetch(uri)

Streaming Large Data

@server.tool()
async def stream_large_dataset(query: str):
    """Stream results for large datasets"""
    async for chunk in database.stream(query):
        yield chunk

Monitoring & Observability

Metrics Collection

from prometheus_client import Counter, Histogram

tool_calls = Counter('mcp_tool_calls', 'Tool invocations', ['tool_name'])
latency = Histogram('mcp_latency', 'Operation latency')

@server.tool()
@latency.time()
async def monitored_tool():
    tool_calls.labels(tool_name='monitored_tool').inc()
    # Tool implementation

Error Tracking

import logging

logger = logging.getLogger("mcp_server")

@server.tool()
async def error_tracked_tool():
    try:
        return await risky_operation()
    except Exception as e:
        logger.error(f"Tool failed: {e}", exc_info=True)
        raise

Testing MCP Servers

Unit Testing

import pytest
from mcp.testing import MockServer

@pytest.mark.asyncio
async def test_customer_tool():
    server = MockServer()
    result = await server.call_tool("get_customer", {"id": "123"})
    assert result["customer_id"] == "123"

Integration Testing

@pytest.mark.asyncio
async def test_full_workflow():
    # Start test server
    async with TestMCPServer() as server:
        # Test resource access
        resource = await server.get_resource("test://data")
        assert resource is not None

        # Test tool execution
        result = await server.call_tool("process_data", {"input": "test"})
        assert result["success"] == True

Decision Framework

Build MCP Server when:

• Creating reusable data/tool integration
• Want AI agents to access your data
• Need standardized interface across frameworks
• Building for ecosystem (others can use your server)

Use existing MCP Server when:

• Connecting to GitHub, Slack, Drive, Postgres, etc.
• Standard data sources with official servers
• Prototyping quickly

Resources

Official:

• Specification: https://modelcontextprotocol.io/specification
• GitHub: https://github.com/modelcontextprotocol
• Server Registry: https://github.com/modelcontextprotocol/servers

SDKs:

• Python: pip install mcp
• TypeScript: npm install @modelcontextprotocol/sdk

---

MCP is the universal standard for AI-to-data integration in 2025 and beyond.