Model Context Protocol (MCP)

Overview

The Model Context Protocol (MCP) is a standardized protocol that enables AI agents to interact with Large Language Models (LLMs) and other AI services in a consistent, efficient manner. MCP abstracts the complexities of different model providers and provides a unified interface for context management, model invocation, and response handling.

Purpose

MCP addresses several key challenges in AI agent development:

Provider Abstraction: Work with multiple LLM providers through a single interface
Context Management: Maintain conversation history and relevant context
State Persistence: Preserve context across multiple interactions
Resource Optimization: Efficient token usage and caching
Error Handling: Standardized error responses and retry mechanisms

Architecture

graph TB
    subgraph "Agent Layer"
        A1[Agent 1]
        A2[Agent 2]
        A3[Agent 3]
    end

    subgraph "MCP Layer"
        MCP[MCP Interface]
        CM[Context Manager]
        MM[Model Manager]
        TM[Token Manager]
        CACHE[Response Cache]
    end

    subgraph "Provider Layer"
        P1[OpenAI]
        P2[Anthropic]
        P3[IBM Watson]
        P4[Azure OpenAI]
        P5[Custom Models]
    end

    subgraph "Storage"
        DB[(Context Store)]
        VCTR[(Vector Store)]
    end

    A1 --> MCP
    A2 --> MCP
    A3 --> MCP

    MCP --> CM
    MCP --> MM
    MCP --> TM
    MCP --> CACHE

    CM --> DB
    CM --> VCTR

    MM --> P1
    MM --> P2
    MM --> P3
    MM --> P4
    MM --> P5

    TM --> MM
    CACHE --> MM

    style MCP fill:#24a148
    style CM fill:#24a148
    style MM fill:#24a148

Core Components

1. MCP Interface

The main entry point for agents to interact with AI models:

from mcp import MCPClient

# Initialize MCP client
mcp = MCPClient(
    provider='openai',
    model='gpt-4',
    api_key=os.getenv('OPENAI_API_KEY')
)

# Send a request
response = mcp.complete(
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "What is the capital of France?"}
    ],
    context_id='conversation-123',
    temperature=0.7,
    max_tokens=150
)

2. Context Manager

Manages conversation history and context:

# Create a new context
context = mcp.context.create(
    context_id='conversation-123',
    metadata={
        'user_id': 'user-456',
        'session_id': 'session-789'
    }
)

# Add messages to context
mcp.context.add_message(
    context_id='conversation-123',
    role='user',
    content='Tell me about AI agents'
)

# Retrieve context
history = mcp.context.get_history(
    context_id='conversation-123',
    limit=10
)

3. Model Manager

Handles model selection and invocation:

# List available models
models = mcp.models.list()

# Get model capabilities
capabilities = mcp.models.get_capabilities('gpt-4')

# Switch models dynamically
mcp.models.set_default('claude-3-opus')

4. Token Manager

Optimizes token usage and manages costs:

# Estimate tokens before sending
token_count = mcp.tokens.estimate(
    messages=messages,
    model='gpt-4'
)

# Get token usage statistics
usage = mcp.tokens.get_usage(
    context_id='conversation-123',
    period='last_24h'
)

Protocol Specification

Request Format

{
  "version": "1.0",
  "context_id": "conversation-123",
  "model": "gpt-4",
  "messages": [
    {
      "role": "system",
      "content": "You are a helpful assistant."
    },
    {
      "role": "user",
      "content": "What is the capital of France?"
    }
  ],
  "parameters": {
    "temperature": 0.7,
    "max_tokens": 150,
    "top_p": 1.0,
    "frequency_penalty": 0.0,
    "presence_penalty": 0.0
  },
  "metadata": {
    "user_id": "user-456",
    "timestamp": "2026-01-15T11:42:00Z"
  }
}

Response Format

{
  "version": "1.0",
  "context_id": "conversation-123",
  "request_id": "req-abc123",
  "model": "gpt-4",
  "response": {
    "role": "assistant",
    "content": "The capital of France is Paris."
  },
  "usage": {
    "prompt_tokens": 25,
    "completion_tokens": 8,
    "total_tokens": 33
  },
  "metadata": {
    "latency_ms": 1250,
    "timestamp": "2026-01-15T11:42:01Z"
  }
}

Error Format

{
  "version": "1.0",
  "context_id": "conversation-123",
  "request_id": "req-abc123",
  "error": {
    "code": "RATE_LIMIT_EXCEEDED",
    "message": "Rate limit exceeded. Please try again later.",
    "retry_after": 60,
    "details": {
      "limit": 100,
      "remaining": 0,
      "reset_at": "2026-01-15T11:43:00Z"
    }
  }
}

Features

1. Multi-Provider Support

Switch between providers seamlessly:

# Use OpenAI
response = mcp.complete(
    provider='openai',
    model='gpt-4',
    messages=messages
)

# Use Anthropic
response = mcp.complete(
    provider='anthropic',
    model='claude-3-opus',
    messages=messages
)

# Use IBM Watson
response = mcp.complete(
    provider='ibm-watson',
    model='gpt-oss-120b',
    messages=messages
)

2. Context Persistence

Maintain context across sessions:

# Save context
mcp.context.save(
    context_id='conversation-123',
    storage='persistent'
)

# Load context later
mcp.context.load(
    context_id='conversation-123'
)

# Resume conversation
response = mcp.complete(
    context_id='conversation-123',
    messages=[{"role": "user", "content": "Continue our discussion"}]
)

3. Streaming Responses

Handle streaming for real-time responses:

# Stream response
for chunk in mcp.stream(
    messages=messages,
    context_id='conversation-123'
):
    print(chunk.content, end='', flush=True)

4. Function Calling

Enable agents to call functions:

# Define functions
functions = [
    {
        "name": "get_weather",
        "description": "Get current weather for a location",
        "parameters": {
            "type": "object",
            "properties": {
                "location": {"type": "string"},
                "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}
            },
            "required": ["location"]
        }
    }
]

# Request with functions
response = mcp.complete(
    messages=messages,
    functions=functions,
    context_id='conversation-123'
)

# Handle function call
if response.function_call:
    result = execute_function(
        response.function_call.name,
        response.function_call.arguments
    )

5. Embeddings

Generate embeddings for semantic search:

# Generate embeddings
embeddings = mcp.embeddings.create(
    input="AI agents are autonomous software entities",
    model="text-embedding-ada-002"
)

# Store in vector database
mcp.embeddings.store(
    embeddings=embeddings,
    metadata={"source": "documentation"},
    collection="knowledge-base"
)

# Semantic search
results = mcp.embeddings.search(
    query="What are AI agents?",
    collection="knowledge-base",
    limit=5
)

Best Practices

1. Context Management

Keep context size manageable (< 8K tokens for most models)
Implement context summarization for long conversations
Clear old contexts regularly to save storage

2. Error Handling

from mcp.exceptions import RateLimitError, ModelError

try:
    response = mcp.complete(messages=messages)
except RateLimitError as e:
    # Wait and retry
    time.sleep(e.retry_after)
    response = mcp.complete(messages=messages)
except ModelError as e:
    # Log error and use fallback
    logger.error(f"Model error: {e}")
    response = fallback_response()

3. Token Optimization

Use appropriate max_tokens limits
Implement response caching for repeated queries
Monitor token usage and costs

4. Security

Never log API keys or sensitive data
Validate and sanitize user inputs
Implement rate limiting per user/session

Integration with IBM Orchestrate

MCP integrates seamlessly with IBM Orchestrate:

from orchestrate import OrchestratePlatform
from mcp import MCPClient

# Initialize both
orchestrate = OrchestratePlatform(...)
mcp = MCPClient(...)

# Register MCP as a service
orchestrate.register_service(
    name='mcp-service',
    service=mcp,
    health_check=mcp.health_check
)

# Use in workflows
workflow = orchestrate.create_workflow(
    name='ai-conversation',
    steps=[
        {
            'name': 'process-input',
            'service': 'mcp-service',
            'method': 'complete',
            'input': '${user.message}'
        }
    ]
)