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IMPLEMENTATION_PLAN.md

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Model Context Protocol Implementation Plan

Project Summary

The Model Context Protocol (MCP) provides a standardized way for AI applications to connect with external data sources and tools. Our Rust implementation has made significant progress, implementing several key features in the specification. This plan outlines the completed work and the remaining tasks required to fully meet the specification.

Current Status

We have implemented:

  • Basic protocol infrastructure with JSON-RPC messaging
  • Tool feature for function execution
  • Resource listing and content retrieval
  • Prompt templates and execution
  • Initial sampling support for LLM integration
  • Resource templates and completion API (✅ Completed)
    • Added completion endpoint with support for both resource templates and prompts
    • Implemented parameter filtering and suggestion logic
    • Created client and server APIs for completion

Remaining Implementation Tasks

1. Enhance URI Template Expansion (Priority: Medium)

  • Improve existing implementation
    • Full RFC 6570 URI template support
    • Validation for template expansion
    • Error handling for malformed templates

2. Roots Feature (Priority: Medium)

  • Add roots capability to client
    • Define roots data structures
    • Implement roots API endpoints
    • Add methods for setting and querying roots
  • Integrate with sampling
    • Context assembly using roots
    • Root prioritization in sampling requests

3. Enhanced Capability Negotiation (Priority: Medium)

  • Improve capability declaration system
    • Add version-specific capability handling
    • Support experimental capabilities
    • Implement backward compatibility checks
  • Add detailed capability information
    • Include version information in capabilities
    • Support capability-specific configuration options

4. Protocol Utilities (Priority: Medium)

  • Progress Reporting
    • Implement progress notification system
    • Add percentage-based progress tracking
    • Support indeterminate progress indicators
  • Cancellation Support
    • Add $/cancelRequest protocol method
    • Implement cancellation token system
    • Add cancellation handlers to long-running operations
  • Configuration
    • Add configuration request/update methods
    • Support notification for configuration changes
    • Implement configuration validation

5. Sampling Enhancements (Priority: High)

  • Improve Model Selection
    • Implement full model preferences system
    • Add support for model hint mapping
    • Support priority-based selection (cost, speed, intelligence)
  • Add Streaming Support
    • Implement token-by-token streaming for sampling
    • Add streaming response handlers
    • Support connection management for long-running streams

6. Security and Trust & Safety (Priority: High)

  • User Consent Flows
    • Add consent request methods
    • Implement progressive disclosure for capabilities
    • Add audit logging for sensitive operations
  • Access Controls
    • Implement resource access control mechanism
    • Add permission scoping for tools
    • Create capability restriction controls

7. Error Handling Standardization (Priority: Medium)

  • Standardize Error Responses
    • Implement all standard error codes
    • Add detailed error information objects
    • Create consistent error handling across features
  • Add Error Recovery Mechanisms
    • Support retry hints for transient errors
    • Add fallback handling for unsupported features

8. Documentation and Examples (Priority: Medium)

  • API Documentation
    • Document all protocol methods
    • Create usage examples for each feature
    • Add sequence diagrams for common workflows
  • Security Guidelines
    • Document security best practices
    • Provide implementation guidance for sensitive operations
  • Example Applications
    • Create comprehensive example applications
    • Demonstrate integration patterns

Implementation Timeline

Phase 1 (Completed)

  • ✅ Complete resource templates and completion API

Phase 2 (Current - Week 3-4)

  • Enhance URI template expansion
  • Implement roots feature
  • Add progress reporting and cancellation
  • Improve capability negotiation

Phase 3 (Week 5-6)

  • Add security and user consent flows
  • Implement streaming sampling support
  • Create configuration system

Phase 4 (Week 7-8)

  • Complete comprehensive testing
  • Write documentation and examples
  • Create demo applications

Development Practices

  1. Testing Strategy

    • Unit tests for all new components
    • Integration tests for cross-component features
    • Example-based tests for API usage

  2. Documentation

    • Document all public APIs
    • Include usage examples
    • Add protocol compliance notes

  3. Versioning

    • Follow semantic versioning
    • Maintain compatibility with protocol versions
    • Document breaking changes

Conclusion

We have made significant progress in implementing the Model Context Protocol specification, with the completion of the resource templates and completion API feature. By continuing to follow this plan, we will create a robust, specification-compliant Rust implementation that enables seamless integration between LLM applications and external data sources and tools.

Next Steps

Based on our current progress, our immediate focus will be:

  1. Enhancing the URI template expansion with full RFC 6570 support
  2. Implementing the roots feature for content organization
  3. Adding progress reporting and cancellation support

These improvements will provide a more robust foundation for the remaining high-priority features like sampling enhancements and security controls.