Designing a Self-Looping MCP Layer: How to enforce trigger conditions from the server side without Agent-host control logic?

[yuanyxh]

Pre-submission Checklist

• I have verified this would not be more appropriate as a feature request in a specific repository
• I have searched existing discussions to avoid duplicates

Your Idea

Hi everyone,

I am currently researching AI Agents and the Model Context Protocol (MCP). I want to build a decoupled, self-looping MCP Shared Knowledge Layer.

My ideal MCP sharing system should be autonomous and self-directed. The MCP layer should actively inject external prompts to guide the Agent on what to do. It should serve as an external MCP knowledge base that any Agent can simply plug into without requiring any custom control logic on the host side.

Essentially, the MCP shared system should dynamically instruct the Agent: when to use which tools to query the knowledge base, and when to extract dialogue context to write back into the knowledge base.

🏗️ Proposed Layered Architecture:

Agent (Orchestrator) ──> LLM (Core Brain) ──> MCP Unified Orchestration (Memory/KB/Tools) ──> Data Layer

• Agent: Core orchestrator with its own native skills/tools.
• LLM: The core brain (pure runtime/stateless processing).
• MCP Shared Service: Shared skills, tools, and knowledge bases.
• Data: Persistent data and knowledge bases.

❓ My Core Question

I need this system to be highly portable and immediately usable by any AI Agent that has implemented the standard MCP protocol.

Given the current state of the MCP ecosystem, is it technically achievable to enforce this type of server-side control flow and prompt injection?

Is it possible to extend the current specification to instruct the Agent to actively fetch specific contexts? Any guidance, architectural suggestions, or feedback would be greatly appreciated! Thank you!!

Scope

• Protocol Specification
• SDK Features
• Documentation
• Developer Experience
• Other