Anthropic Model Context Protocol (MCP)

A Anthropic Model Context Protocol (MCP) is an open-source communication protocol that enables standardized integration between LLM applications and external systems (to support data access and tool interaction capabilities).

• Context:
  • It can (typically) perform Integration Functions through protocol interfaces:
    • Protocol Input: data requests, action commands, configuration settings
    • Protocol Output: retrieved data, action results, status updates
    • Protocol Performance Measure: integration speed, connection reliability, data accuracy
  • It can (typically) enable Data Integration through standardized protocols.
  • It can (typically) maintain Bidirectional Communication through protocol channels.
  • It can (typically) support System Interoperability through protocol standards.
  • It can (typically) implement Integration Security through protocol controls.
  • It can (typically) define a Protocol Layer MCP Component through:
    • Protocol Class Component for handling Message Framing and Request Response Linking
    • Client Class Component for Client Management
    • Server Class Component for Server Operations
  • It can (typically) define a Transport Layer MCP Component through:
    • Stdio Transport Component for enabling Local Process Communication.
    • HTTP SSE Transport Component for managing Server Client Messaging.
    • JSON RPC Transport Component for standardizing Message Exchange Format.
  • It can (typically) define a Message Type MCP Component through:
    • Request Message Component for implementing Response Expected Messages.
    • Notification Message Component for handling One Way Messages.
    • Result Message Component for delivering Success Response Messages.
    • Error Message Component for managing Failed Request Messages.
  • It can (typically) define a Core Architecture MCP Component through:
    • Host Component for providing LLM Application Interface.
    • Client Component for maintaining Server Connection State.
    • Server Component for delivering Context Tool Services.
  • It can (typically) define an Error Handling MCP Component through:
    • Standard Error Code Component for standardizing Error Classification System.
    • Error Propagation Component for enabling Error Communication Flow.
    • Error Response Component for managing Request Failure Protocol.
  • It can (typically) define a Message Exchange MCP Component through:
    • Request Response Pattern Component for handling Bidirectional Communication.
    • Notification Pattern Component for managing Unidirectional Messages.
    • Connection Lifecycle Component for controlling Protocol State Management.
  • It can (often) provide Server Implementation through TypeScript SDK and Python SDK.
  • It can (often) support Client Development through frontend SDKs and backend SDKs.
  • It can (often) enable Cloud Platform Integration through pre-built servers for Google Drive and Dropbox.
  • It can (often) facilitate Team Collaboration through Slack integration and GitHub integration.
  • It can (often) support Development Testing through protocol inspectors and debug tools.
  • It can (often) provide Implementation Guidance through reference documentation and API specifications.
  • ...
  • It can range from being a Local Development Implementation to being a Cloud-Based Implementation, depending on its deployment context.
  • It can range from being a Simple Data Connector to being a Complex Workflow Engine, depending on its integration complexity.
  • ...
  • It can be developed through Community Contribution.
  • It can be implemented via Protocol SDKs.
  • It can be verified through Protocol Testing.
  • It can maintain Protocol Version state.
  • ...
• Examples:
  • MCP Implementations, such as:
    • Desktop Implementations, such as:
      • Claude Desktop App Integration for local execution.
      • Local Development Server for testing integrations.
    • Enterprise Implementations, such as:
      • Slack MCP Integration for team communication.
      • GitHub MCP Integration for code repository access.
      • Google Drive MCP Integration for document management.
  • MCP Tools, such as:
    • Development Tools, such as:
      • MCP Inspector for protocol debugging.
      • MCP SDKs for server development.
    • Integration Tools, such as:
      • Puppeteer MCP Integration for web scraping.
      • File System MCP Integration for local file access.
  • MCP Versions, such as:
    • MCP v2024-11-29 for current implementation.
  • ...
• Counter-Examples:
  • OpenAI Plugin Standard, which lacks protocol standardization and widespread adoption.
  • One-Way Data Protocols, which lack bidirectional capability.
  • Custom Integration Solutions, which lack standardized interfaces.
• See: Integration Protocol, AI System, Open Source Protocol, Data Integration, Protocol Standard, JSON-RPC 2.0 Protocol.

---

---

References

2024-12-11

• https://modelcontextprotocol.io/introduction
  • Core Definition:
    • MCP functions like a "USB-C port for AI applications," providing a universal standard for connecting LLM systems with various data sources and tools
    • MCP follows a client-server architecture where a single host application can maintain connections with multiple MCP servers simultaneously
    • MCP offers flexibility in LLM provider selection, allowing organizations to switch between different vendors while maintaining their integrations
    • MCP enables the creation of reusable prompt templates and workflows, helping standardize LLM interactions across different applications

2024-12-11

• Perplexity.ai
  • Protocol Architecture:
    • Request Components:
      • Prompt Input: User's query or command in structured format
      • Contextual Data References: Links to external documents, databases, or APIs
      • Instruction Fields: Instructions for guiding model output
    • Response Components:
      • Generated Content: Model's output in standardized format
      • Response Metadata: Including tokens used, computation time, and model version
  • Metadata Standardization:
    • Metadata Fields:
      • Token Usage Counts for cost monitoring
      • Timing Data for performance tracking
      • Model Version and Configuration details
      • Reference IDs to external sources

2024-12-01

• https://www.youtube.com/watch?v=8mU2OeOCIrE
  • NOTES:
    • MCP's current local implementation prioritizes local execution through the Claude desktop app, while its system architecture is designed to scale to cloud-based microservices and cloud functions
    • MCP's permission model implements Integration Security through a granular approval system, where system users must explicitly authorize each tool action
    • MCP represents a strategic shift from OpenAI plugin to an open-source protocol that explicitly invites use by competing AI models
    • MCP's system architecture anticipates future system integration with low-code platforms and no-code platforms and AI agent development tools

2024-11-29

• https://github.com/modelcontextprotocol
  • NOTES:
    • Development tools such as the MCP Inspector and debugging guides facilitate server testing and integration validation
    • Contributing to MCP is encouraged, with detailed guides and a community forum available to support collaboration

2024-11-25

• https://www.anthropic.com/news/model-context-protocol
  • QUOTES:
    • "Open technologies like the Model Context Protocol are the bridges that connect AI to real-world applications, ensuring innovation is accessible, transparent, and rooted in collaboration."
  • NOTES:
    • Early adopters, including Block and Apollo, demonstrate MCP's practical applicability
    • Open-source architecture fosters collaboration and innovation within the developer community

---