MCP (Model Context Protocol) Deep Dive

Model Context Protocol (MCP) is the protocol used by ByteBuddy to standardize AI model interactions, providing a unified interface to access various AI services and features.

MCP Overview

Protocol Goals

MCP aims to achieve:

• Standardized Interface: Unified API calling method
• Provider Agnostic: Support multiple AI providers
• Feature Extension: Easy to add new features
• Compatibility: Backward compatibility guarantee

Core Concepts

• Provider: AI service provider
• Model: Specific AI model
• Role: Model usage scenario
• Capability: Features supported by model

Basic Configuration

Standard MCP Configuration

models:
  - name: "mcp-chat-model"
    provider: "openai"
    model: "gpt-4"
    apiKey: "${OPENAI_API_KEY}"
    roles: ["chat"]
    capabilities: ["tool_use"]
    defaultCompletionOptions:
      temperature: 0.7
      maxTokens: 2000

Configuration Explanation

• name: Unique identifier for the model
• provider: AI service provider
• model: Specific model name
• apiKey: API authentication key
• roles: Roles supported by the model
• capabilities: Special abilities of the model

Role System

Supported Roles

models:
  # Chat role
  - name: "chat-model"
    provider: "openai"
    model: "gpt-4"
    apiKey: "${OPENAI_API_KEY}"
    roles: ["chat"]
    defaultCompletionOptions:
      temperature: 0.7
      maxTokens: 2000

  # Edit role
  - name: "edit-model"
    provider: "anthropic"
    model: "claude-3-sonnet"
    apiKey: "${ANTHROPIC_API_KEY}"
    roles: ["edit"]
    defaultCompletionOptions:
      temperature: 0.3
      maxTokens: 4000

  # Autocomplete role
  - name: "autocomplete-model"
    provider: "together"
    model: "codellama/CodeLlama-13b-Instruct-hf"
    apiKey: "${TOGETHER_API_KEY}"
    roles: ["autocomplete"]
    defaultCompletionOptions:
      temperature: 0.1
      maxTokens: 256

  # Apply role
  - name: "apply-model"
    provider: "openai"
    model: "gpt-4"
    apiKey: "${OPENAI_API_KEY}"
    roles: ["apply"]
    defaultCompletionOptions:
      temperature: 0.5
      maxTokens: 4096

  # Embed role
  - name: "embed-model"
    provider: "openai"
    model: "text-embedding-3-large"
    apiKey: "${OPENAI_API_KEY}"
    roles: ["embed"]

  # Rerank role
  - name: "rerank-model"
    provider: "cohere"
    model: "rerank-english-v3.0"
    apiKey: "${COHERE_API_KEY}"
    roles: ["rerank"]

Multi-Role Configuration

A single model can support multiple roles:

models:
  - name: "versatile-model"
    provider: "openai"
    model: "gpt-4"
    apiKey: "${OPENAI_API_KEY}"
    roles: ["chat", "edit", "apply"]
    defaultCompletionOptions:
      temperature: 0.6
      maxTokens: 4000

Capability System

Tool Use Capability

models:
  - name: "tool-capable-model"
    provider: "openai"
    model: "gpt-4"
    apiKey: "${OPENAI_API_KEY}"
    roles: ["chat"]
    capabilities: ["tool_use"]
    defaultCompletionOptions:
      temperature: 0.7
      maxTokens: 4000

Tool use includes:

• Function Calling: Call external functions
• API Integration: Access external APIs
• Data Retrieval: Query databases
• File Operations: Read and write files

Image Input Capability

models:
  - name: "vision-model"
    provider: "openai"
    model: "gpt-4-vision-preview"
    apiKey: "${OPENAI_API_KEY}"
    roles: ["chat"]
    capabilities: ["image_input"]
    defaultCompletionOptions:
      temperature: 0.7
      maxTokens: 4096

Next Edit Capability

models:
  - name: "next-edit-model"
    provider: "anthropic"
    model: "claude-3-sonnet"
    apiKey: "${ANTHROPIC_API_KEY}"
    roles: ["edit"]
    capabilities: ["next_edit"]
    defaultCompletionOptions:
      temperature: 0.3
      maxTokens: 4000

Request Options

Basic Request Options

models:
  - name: "configured-model"
    provider: "openai"
    model: "gpt-4"
    apiKey: "${OPENAI_API_KEY}"
    roles: ["chat"]
    requestOptions:
      timeout: 60000 # 60 seconds
      maxRetries: 3
      retryDelay: 1000 # 1 second
    defaultCompletionOptions:
      temperature: 0.7
      maxTokens: 2000

Custom Request Headers

models:
  - name: "custom-headers-model"
    provider: "openai-compatible"
    model: "custom-model"
    apiKey: "${API_KEY}"
    apiBase: "https://api.example.com/v1"
    requestOptions:
      headers:
        "X-Custom-Header": "value"
        "User-Agent": "ByteBuddy/1.0"
    roles: ["chat"]
    defaultCompletionOptions:
      temperature: 0.7
      maxTokens: 2000

Completion Options

Standard Completion Options

models:
  - name: "tuned-model"
    provider: "openai"
    model: "gpt-4"
    apiKey: "${OPENAI_API_KEY}"
    roles: ["chat"]
    defaultCompletionOptions:
      temperature: 0.7 # Creativity (0-2)
      maxTokens: 2000 # Maximum tokens
      topP: 0.9 # Nucleus sampling
      frequencyPenalty: 0.0 # Frequency penalty (-2 to 2)
      presencePenalty: 0.0 # Presence penalty (-2 to 2)

Advanced Completion Options

models:
  - name: "advanced-model"
    provider: "openai"
    model: "gpt-4"
    apiKey: "${OPENAI_API_KEY}"
    roles: ["chat"]
    defaultCompletionOptions:
      temperature: 0.7
      maxTokens: 4000
      topP: 0.95
      topK: 50
      frequencyPenalty: 0.1
      presencePenalty: 0.1
      stopSequences: ["\n\n", "###"]

Provider-Specific Configuration

Azure OpenAI

models:
  - name: "azure-model"
    provider: "azure-openai"
    model: "gpt-4"
    apiKey: "${AZURE_OPENAI_API_KEY}"
    apiBase: "${AZURE_OPENAI_API_BASE}"
    env:
      deploymentName: "gpt-4-deployment"
      apiVersion: "2024-02-15-preview"
    roles: ["chat"]
    defaultCompletionOptions:
      temperature: 0.7
      maxTokens: 4000

AWS Bedrock

models:
  - name: "bedrock-model"
    provider: "bedrock"
    model: "anthropic.claude-3-sonnet-20240229-v1:0"
    env:
      region: "us-east-1"
      accessKeyId: "${AWS_ACCESS_KEY_ID}"
      secretAccessKey: "${AWS_SECRET_ACCESS_KEY}"
    roles: ["chat"]
    defaultCompletionOptions:
      temperature: 0.7
      maxTokens: 4096

Google Vertex AI

models:
  - name: "vertexai-model"
    provider: "vertexai"
    model: "gemini-pro"
    env:
      projectId: "${GOOGLE_CLOUD_PROJECT_ID}"
      location: "us-central1"
      credentials: "${GOOGLE_APPLICATION_CREDENTIALS}"
    roles: ["chat"]
    defaultCompletionOptions:
      temperature: 0.7
      maxTokens: 2048

MCP Extensions

Custom Providers

models:
  - name: "custom-provider"
    provider: "openai-compatible"
    model: "custom-model"
    apiKey: "${CUSTOM_API_KEY}"
    apiBase: "https://api.custom.com/v1"
    roles: ["chat"]
    defaultCompletionOptions:
      temperature: 0.7
      maxTokens: 2000

Plugin System

ByteBuddy supports MCP plugin integration:

• Tool Plugins: Add new tool features
• Provider Plugins: Support new AI providers
• Capability Plugins: Extend model capabilities
• Middleware Plugins: Request/response processing

Best Practices

1. Model Selection

• Choose appropriate model based on task
• Balance performance and cost
• Consider latency requirements
• Evaluate quality needs

2. Parameter Tuning

• Start with default values
• Adjust based on feedback
• Document best configurations
• Regularly re-evaluate

3. Error Handling

• Configure appropriate timeouts
• Implement retry mechanisms
• Log error messages
• Provide fallback options

4. Security

• Use environment variables for keys
• Limit API call frequency
• Monitor usage
• Rotate keys regularly

Troubleshooting

Common Issues

Invalid Configuration

Solutions:

• Check YAML syntax
• Verify required fields
• Confirm environment variables set

API Call Failures

Solutions:

• Verify API key
• Check network connectivity
• Confirm model name correct
• Review provider status

Performance Issues

Solutions:

• Optimize token limits
• Adjust timeout settings
• Consider using faster models
• Implement caching strategy

Environment Variables

# ~/.bashrc or ~/.zshrc
export OPENAI_API_KEY="your-openai-api-key"
export ANTHROPIC_API_KEY="your-anthropic-api-key"
export TOGETHER_API_KEY="your-together-api-key"
export COHERE_API_KEY="your-cohere-api-key"

# Cloud service providers
export AZURE_OPENAI_API_KEY="your-azure-key"
export AZURE_OPENAI_API_BASE="https://your-resource.openai.azure.com"
export AWS_ACCESS_KEY_ID="your-aws-access-key"
export AWS_SECRET_ACCESS_KEY="your-aws-secret-key"
export GOOGLE_CLOUD_PROJECT_ID="your-project-id"
export GOOGLE_APPLICATION_CREDENTIALS="/path/to/credentials.json"

Through understanding and correctly using MCP, you can fully leverage ByteBuddy's powerful features to build an efficient AI-assisted development environment.