Server Ollama

This repository contains a small blog-style application with two working flows, both using decoupled architecture with JSON-RPC:

1. AI Chatbot Flow (recommended for production): Natural language requests are converted to JSON-RPC by the AI, then executed through the MCP protocol.
2. MCP Client Flow: An MCP-compatible client directly calls the MCP endpoint with JSON-RPC requests.

The project is split into two services:

• Main REST API server: server.js
• MCP server with Ollama integration: mcp-server/server.js

What the app does

The main API stores and manages posts and comments in MongoDB.

• Posts contain: title, author, category, body, createdAt
• Comments belong to a post and contain: postId, text, commenter, createdAt

The MCP server adds two higher-level interaction paths on top of that REST API.

---

Architecture: Decoupled via JSON-RPC

Flow 1: AI Chatbot -> JSON-RPC -> MCP -> REST API -> MongoDB

This is the decoupled, production-recommended flow used when a user talks to the chatbot interface or sends a request to the AI endpoint.

Why this architecture?

• AI Layer (Ollama): Focuses ONLY on understanding user intent and generating JSON-RPC requests. Does NOT know about backend details.
• MCP Layer: Acts as middleware to execute JSON-RPC requests using the standard MCP protocol.
• Backend Layer: REST API and database remain isolated from AI logic.

Flow Steps:

1. A user sends a message such as "Create a new post..." to the chatbot UI or to the /ai-chatbot endpoint.
2. The MCP server sends the message to Ollama.
3. Ollama understands the intent and generates a JSON-RPC 2.0 request to call the appropriate MCP tool (e.g., create_post).
4. The MCP client executes that JSON-RPC request through the MCP protocol.
5. The appropriate MCP tool is invoked, which calls the REST API.
6. The REST API updates MongoDB and returns the result.

Separation of Concerns:

User Message
    ↓
[AI Layer] Ollama 
  → Understands intent 
  → Generates JSON-RPC request
    ↓
[MCP Layer] MCP Client 
  → Sends JSON-RPC to MCP Server 
  → Executes tool
    ↓
[Backend Layer] REST API 
  → MongoDB

This path is used by:

• the chatbot page at public/chatbot.html
• the endpoint POST /ai-chatbot on the MCP server

Flow 2: MCP Client -> /mcp -> MCP Tools -> REST API -> MongoDB

This flow is used when an MCP-compatible client connects to the MCP server directly.

1. The MCP client sends a JSON-RPC request to POST /mcp.
2. The MCP server handles initialization and tool calls.
3. The server invokes registered tools such as create_post, list_posts, update_post, delete_post, add_comment, and list_comments.
4. Each tool calls the main REST API.
5. The REST API performs the action in MongoDB.

This is the path used by MCP-compatible clients.

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Key Differences: Tight Coupling vs. Decoupled

Aspect	Before (Tight Coupling)	After (Decoupled)
AI Responsibility	Directly calls REST API endpoints	Only understands intent, generates JSON-RPC
Coupling	AI tightly bound to backend structure	AI and backend completely decoupled
Protocol	Direct HTTP calls	Standard JSON-RPC 2.0 protocol
Middleware	None	MCP layer acts as middleware
Scalability	Difficult to replace AI or backend	Easy to swap AI model or backend service
Production Ready	Not recommended ❌	✓ Production-ready ✓
Standard Compliance	Proprietary	JSON-RPC 2.0 + MCP Standard

---

How JSON-RPC Decoupling Works

Example: User says "Create a tech post about AI"

Step 1: AI generates JSON-RPC request (not direct execution)

{
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "name": "create_post",
    "arguments": {
      "title": "Understanding AI",
      "author": "John Doe",
      "category": "tech",
      "body": "Artificial Intelligence is transforming how we work and live..."
    }
  },
  "id": 1
}

Key Point: Ollama ONLY generates this JSON-RPC request. It does NOT execute it directly.

Step 2: MCP Client sends this JSON-RPC to MCP Server The MCP client (in the MCP server itself) sends this JSON-RPC request to the /mcp endpoint.

Step 3: MCP Server executes the tool The MCP server routes the create_post tool call, which validates the input and calls the REST API.

Step 4: REST API persists to MongoDB

POST /posts → Validation → MongoDB.insert() → Returns created post

Step 5: Result flows back

MCP Server → MCP Client → Chatbot UI → User sees the created post

---

Benefits of This Architecture

1. Loose Coupling

You can independently:

• Replace Ollama with another AI model (GPT, Claude, etc.) without changing backend
• Switch backend from REST API to gRPC without changing AI
• Deploy AI and backend in different regions or cloud providers
• Use different programming languages for each layer

2. Scalability

• Load balance MCP layer independently from AI and backend
• Cache AI responses without affecting backend performance
• Implement retry logic and circuit breakers at MCP level
• Scale AI horizontally without scaling backend

3. Testability

• AI layer testable independently (mock MCP)
• MCP layer testable independently (mock REST API)
• Backend testable independently (mock MCP layer)
• Clear unit test boundaries

4. Standards Compliance

• Uses standard JSON-RPC 2.0 protocol (not proprietary)
• Compatible with any MCP-compliant client
• Follows Model Context Protocol specification
• Can integrate with other MCP servers

5. Production Ready

• Proper separation of concerns
• Clear error boundaries and logging
• Middleware pattern enables monitoring at MCP level
• Request tracing across layers
• No tight coupling risks

6. Maintainability

• Changes to backend don't affect AI logic
• Updates to AI model don't require backend changes
• Clear interfaces between layers (JSON-RPC)
• Easier debugging with layer isolation

---

Available MCP Tools

The MCP server exposes the following tools:

• create_post: {title, author, category, body} - Creates a new post
• list_posts: {} - Lists all posts
• get_post: {postId} - Gets a single post by ID
• update_post: {postId, title, author, category, body} - Updates an existing post
• delete_post: {postId} - Deletes a post (WARNING: deletes post and ALL comments)
• add_comment: {postId, text, commenter} - Adds a comment to a post
• list_comments: {postId} - Lists all comments for a post

---

Implementation Details

AI Layer (processUserMessage)

• Receives user message and context
• Sends to Ollama with system prompt focused on intent understanding
• Ollama generates a JSON-RPC 2.0 request (not action plan)
• Returns JSON-RPC request WITHOUT executing it

MCP Layer (executeMcpToolViaJsonRpc)

• Receives JSON-RPC request from AI layer
• Validates tool name and arguments
• Executes appropriate tool logic
• Returns JSON-RPC 2.0 response

Backend Layer (/posts endpoints)

• Handles REST API requests from MCP layer
• Performs MongoDB operations
• Returns results to MCP layer

Endpoints

• POST /ai-chatbot: Accepts user message, returns AI-generated JSON-RPC + execution result
• POST /mcp: Standard MCP protocol endpoint for MCP clients
• GET /health: Health check endpoint

---

Response Format from /ai-chatbot

{
  "success": true,
  "jsonRpcRequest": {
    "jsonrpc": "2.0",
    "method": "tools/call",
    "params": {
      "name": "create_post",
      "arguments": {...}
    },
    "id": 1
  },
  "jsonRpcResponse": {
    "jsonrpc": "2.0",
    "result": {...},
    "id": 1
  },
  "executionTime": "245ms"
}

This response shows:

1. The JSON-RPC request AI generated
2. The JSON-RPC response from tool execution
3. Execution time for monitoring