Decentralized Context Protocol
Overview Diagram
This diagram illustrates the flow of information in the Decentralized Context Protocol. User input first passes through an intent engine, which identifies the goal of the message. The protocol then coordinates access to relevant context by checking identity, permissions, and retrieving data from decentralized sources. Finally, the compiled context is passed to the AI model for a personalized and secure response.
Enabling Smart Context
The Decentralized Context Protocol offers a new way to manage and present user context. It also enables AI agents to be responsive and aware of past interactions. Instead of storing everything on a single server, MindCP distributes elements of user context across verifiable, decentralized systems.
This architecture eliminates the need for centralized storage and allows users to maintain ownership of their data and digital identity. It also allows users to interact with more agents, platforms, or models.
The protocol is flexible and developer-friendly. It also allows for new types of data (on-chain activity, past actions, task logs, reputation, preferences) to be added if needed.
Key Advantages
Privacy-first intelligence AI agents gain context without holding or storing user data long-term. Context is requested only when needed and under clearly defined permissions.
Portability and continuity Users can move across different applications while keeping their preferences and history intact. The agent does not need to relearn the user from scratch.
Composable integration Any application or developer can request context modules relevant to their use case. The system is modular and expandable.
Trustless coordination Since all context calls are signed, logged, and evaluated via policies, users can audit how their data is being used at any time.
Example Use Cases
Portfolio Tracking Agent
User asks: “What was my staking yield over the past week?”
The intent engine tags the query as a portfolio summary request
The agent requests staking-related context (wallets, activity logs)
The protocol checks if this agent is authorized to access this data
Context is fetched from decentralized sources
The AI model provides a personalized answer based on this context
Task Assistant Agent
User says: “Remind me to submit the grant proposal on Friday”
The agent detects the intent as a recurring reminder
A context request is made for calendar access and reminder preferences
Access is approved via permission logic
Context is compiled and the task is scheduled accordingly
In both scenarios, AI agents act with awareness of the user’s history, identity, and intent, without ever storing or controlling the data themselves. The Decentralized Context Protocol enables this balance between intelligence and privacy.
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