Write mermaid code for architecture and a ladder chart showing AI communication between:
- User
- AI Client
- LLM
- MCP Server
- MCP Registry
- Multiple MCP Agents

Include:
 - All components and their roles.
 - Communication paths with protocol labels (e.g., JSON-RPC, gRPC, message bus).
 - Agent-to-agent communication.
 - Sequence of interactions from user query to final response.
 - Annotate steps like query, lookup, discovery, execution, result, response.

graph TD
   subgraph User_Interaction
       U[User] -->|Query JSON-RPC| AI_Client
   end

   subgraph AI_System
       AI_Client -->|Request gRPC| LLM
       LLM -->|Lookups gRPC| MCP_Server
       MCP_Server -->|Lookup Data| MCP_Registry
       MCP_Registry -->|Discovery| MCP_Agent1
       MCP_Registry -->|Discovery| MCP_Agent2
       MCP_Registry -->|Discovery| MCP_Agent3
   end

   subgraph Agent_Communication
       MCP_Agent1 -->|Execute gRPC| LLM
       MCP_Agent2 -->|Execute gRPC| LLM
       MCP_Agent3 -->|Execute gRPC| LLM
   end

   subgraph Final_Response
       LLM -->|Results gRPC| MCP_Server
       MCP_Server -->|Final Result JSON-RPC| AI_Client
       AI_Client -->|Response JSON-RPC| U
   end

   classDef blue fill:#66ccff,stroke:#333,stroke-width:1px;
   class U,AI_Client,LLM,MCP_Server,MCP_Registry,MCP_Agent1,MCP_Agent2,MCP_Agent3 blue;

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sequenceDiagram
    participant U as User
    participant AI as AI Client
    participant LLM as LLM
    participant MCP as MCP Server
    participant Reg as MCP Registry
    participant A1 as MCP Agent 1
    participant A2 as MCP Agent 2
    participant A3 as MCP Agent 3

    U->>AI: Query (JSON-RPC)
    AI->>LLM: Forward Query (gRPC)
    LLM->>MCP: Lookup Request (gRPC)
    MCP->>Reg: Query MCP Registry (gRPC)
    Reg->>A1: Discover Agents (gRPC)
    Reg->>A2: Discover Agents (gRPC)
    Reg->>A3: Discover Agents (gRPC)
    A1->>LLM: Execute Task (gRPC)
    A2->>LLM: Execute Task (gRPC)
    A3->>LLM: Execute Task (gRPC)
    LLM->>MCP: Return Results (gRPC)
    MCP->>AI: Send Result (JSON-RPC)
    AI->>U: Response (JSON-RPC)

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---

prompt 2

Assume some of the mcp agents also interacts with llm for its operation, Not all agents need to be connected to LLM, maybe show a typical example with different LLM's and an agent without LLM interaction, provide a typical use case diagram, with the prompt etc. Also show the sequence like if the MCP registry interaction and the client happens first? add time line of the events with adding 1, 2, 3 on top of the arrows

graph TD
    %% User & Client Layer
    U[User] -->|1. Query JSON-RPC| AI_Client[AI Client]

    %% AI System Layer
    AI_Client -->|2. Request gRPC| LLM_Core[LLM Core]
    AI_Client -->|2. Request gRPC| LLM_Specialist[LLM Specialist]

    %% Core LLM Interaction
    LLM_Core -->|3. Task Lookup gRPC| MCP_Server[MCP Server]

    %% MCP Discovery
    MCP_Server -->|4. Registry Lookup gRPC| MCP_Registry[MCP Registry]
    MCP_Registry -->|5. Discover Agents - message bus| MCP_Agent1[Agent 1 - Data Retriever]
    MCP_Registry -->|5. Discover Agents - message bus| MCP_Agent2[Agent 2 - LLM Enhanced]
    MCP_Registry -->|5. Discover Agents - message bus| MCP_Agent3[Agent 3 - Tool Executor]

    %% Agent Interaction Layer
    MCP_Server -->|6. Dispatch Task gRPC| MCP_Agent1
    MCP_Server -->|6. Dispatch Task gRPC| MCP_Agent2
    MCP_Server -->|6. Dispatch Task gRPC| MCP_Agent3

    %% LLM collaboration by selective agents
    MCP_Agent2 -->|7. Query for Reasoning gRPC| LLM_Specialist

    %% Result Return Flow
    MCP_Agent1 -->|8. Result JSON-RPC| MCP_Server
    MCP_Agent2 -->|8. Result gRPC| MCP_Server
    MCP_Agent3 -->|8. Result JSON-RPC| MCP_Server

    MCP_Server -->|9. Aggregate Results| LLM_Core
    LLM_Core -->|10. Final Response gRPC| AI_Client
    AI_Client -->|11. Response JSON-RPC| U

    classDef entity fill:#66ccff,stroke:#003366,stroke-width:1px,color:#000;
    classDef llm fill:#aaf0d1,stroke:#005544,stroke-width:1px,color:#000;
    class U,AI_Client,MCP_Server,MCP_Registry,MCP_Agent1,MCP_Agent2,MCP_Agent3 entity;
    class LLM_Core,LLM_Specialist llm;

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sequenceDiagram
    participant U as User
    participant AI as AI Client
    participant LLM1 as LLM-Core
    participant LLM2 as LLM-Specialist
    participant MCP as MCP Server
    participant Reg as MCP Registry
    participant A1 as Agent 1 (Data Retriever)
    participant A2 as Agent 2 (LLM-Enhanced)
    participant A3 as Agent 3 (Tool Executor)

    %% Step 1
    U->>+AI: 1️⃣ Submit Query (JSON-RPC)
    %% Step 2
    AI->>+LLM1: 2️⃣ Forward Query (gRPC)
    %% Step 3
    LLM1->>+MCP: 3️⃣ Request Task Lookup (gRPC)
    %% Step 4
    MCP->>+Reg: 4️⃣ Discover Available Agents (gRPC)
    %% Step 5
    Reg-->>MCP: 5️⃣ Return List of Agents
    %% Step 6
    MCP->>+A1: 6️⃣ Dispatch Retrieval Task (gRPC)
    MCP->>+A2: 6️⃣ Dispatch Analysis Task (gRPC)
    MCP->>+A3: 6️⃣ Dispatch Execution Task (gRPC)
    %% Step 7
    A2->>+LLM2: 7️⃣ Request Reasoning Help (gRPC)
    LLM2-->>A2: 7️⃣ Return Insights
    %% Step 8
    A1-->>MCP: 8️⃣ Send Raw Data (JSON-RPC)
    A2-->>MCP: 8️⃣ Send Refined Output (gRPC)
    A3-->>MCP: 8️⃣ Send Execution Result (JSON-RPC)
    %% Step 9
    MCP-->>LLM1: 9️⃣ Aggregate + Summarize Results
    %% Step 10
    LLM1-->>AI: 🔟 Final Response (gRPC)
    AI-->>U: 🏁 Present Answer (JSON-RPC)

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