Model Context Protocol (MCP)

EcoAI’s Model Context Protocol (MCP) is the coordination layer that transforms raw user input into structured, memory-aware AI reasoning. Unlike conventional architectures that bind prompt logic tightly within model code or application logic, MCP acts as a decoupled compiler, aligning semantic intent with contextual memory and execution policy.

When a user initiates an interaction, MCP processes this input through a defined series of abstraction layers: semantic parsing, topic resolution, dNFT binding, and task-specific prompt generation. This ensures that agents not only respond based on the current prompt, but anchor their reasoning in relevant historical memory — dynamically and deterministically.

• Semantic Parsing Graph: Translates natural language input into machine-readable instruction trees.

• Context Resolution Engine: Maps parsed intent to specific dNFTs via topic and permission filters.

• Prompt Compiler Layer: Renders context-weighted prompts structured for different LLM interfaces.

• Logic Injection Hooks: Embeds agent-specific policy rules, tools, or task conditions at runtime.

• Stateless Multi-Agent Routing: Enables concurrent prompt assembly across agents with shared or distinct memory pools.

By abstracting prompt construction and memory access into a dedicated protocol layer, MCP enables modular agent design, deterministic reasoning flows, and scalable orchestration. This architectural separation of logic, memory, and model allows EcoAI to support complex, composable agents that are fully interoperable — regardless of the underlying AI engine.

MCP is not just a middleware — it is the execution logic compiler that turns decentralized memory into actionable, policy-aligned intelligence.