MCP Standards in a nutshell

 1. Request and Response Formats

MCP aims to standardize the structure of requests sent to models and the responses they return. This includes:

Input Formats: Ensuring models can process queries in a common format, regardless of the vendor.

Output Formats: Defining a consistent structure for model responses, including metadata like confidence scores, provenance information, and structured data (e.g., JSON).

Error Handling: Standardized error codes and messages for better debugging and reliability.

2. Context Sharing and State Management

MCP proposes mechanisms to manage and share context between models or sessions, such as:

Memory Persistence: How context is maintained across multiple queries.

Session Management: Allowing continuity in conversations or tasks by persisting user-defined context.

Global Context: Enabling multiple models or tools to access shared context seamlessly.

3. Compatibility Across Tools and APIs

The protocol aims to bridge different vendor ecosystems by:

Unified API Interfaces: A single API specification that can be implemented by all participating models.

Interoperability Standards: Enabling models, vector databases, and tools to work together in workflows like retrieval-augmented generation (RAG) without vendor lock-in.

4. Metadata and Provenance Standards

MCP emphasizes the importance of detailed metadata in model responses, including:

Source Attribution: Where information comes from, especially in multi-source systems.

Confidence Scores: How certain the model is about its outputs.

Execution Logs: Tracing the steps taken to generate a response.

5. Tool Interactions and Plugin Standards

MCP proposes standards for how models interact with external tools, databases, and APIs, including:

Plugin Interfaces: Defining a unified way to integrate tools (e.g., calculators, retrieval systems).

Execution Standards: How models should invoke tools and handle tool responses.

6. Security and Privacy

Establishing protocols to ensure:

Secure Data Transmission: Encrypting queries and responses.

Access Control: Defining who can interact with the model or tools.

Compliance: Adhering to legal and ethical standards for data handling.

7. Evaluation and Logging Standards

Proposals for how to:

Benchmark Models: Using standardized datasets or metrics.

Log Interactions: Tracking user-model interactions for auditing or improving system behavior.

Summary

MCP is essentially proposing a holistic standard that covers:

Request/Response Formats

Context and State Management

Interoperability Across Vendors and Tools

Metadata and Provenance

Security and Compliance

Tool and Plugin Interactions

By addressing these areas, MCP aims to create a more unified, efficient, and user-friendly ecosystem for working with AI models. However, its adoption depends on industry-wide collaboration and agreement.