25 May Components of Model Context Protocol (MCP)
The Model Context Protocol (MCP) is a structured approach to defining and managing the context in which machine learning (ML) or AI models operate. It ensures models are deployed with proper situational awareness, governance, and adaptability. While MCP is not a universally standardized framework, its key components typically include:
1. Contextual Metadata
- Model identity (name, version, purpose)
- Owner/developer information
- Creation and modification timestamps
- Domain-specific tags (e.g., healthcare, finance)
2. Operational Context
- Input/Output Specifications: Data formats, schemas, and constraints.
- Environment Details: Deployment setting (cloud, edge, on-premises).
- Dependencies: Libraries, APIs, or services the model relies on.
3. Governance & Compliance
- Regulatory requirements (GDPR, HIPAA, etc.).
- Ethical guidelines and fairness constraints.
- Audit trails for accountability.
4. Performance & Safety Boundaries
- Acceptable operating ranges (e.g., data drift thresholds).
- Fallback mechanisms if the model fails.
- Guardrails to prevent harmful outputs.
5. Dynamic Adaptation Rules
- Rules for model retraining or version updates.
- Triggers for context shifts (e.g., new data distributions).
6. Stakeholder Communication
- Documentation for end-users, developers, and auditors.
- Alerts and notifications for anomalies.
7. Security & Access Control
- Authentication/authorization for model access.
- Data encryption and model protection measures.
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