Akib Dabgar MCP Server

A production-ready, TypeScript-based Model Context Protocol (MCP) server built for Akib Dabgar. The server acts as a structured personal knowledge hub, allowing LLM clients (such as Claude Desktop, ChatGPT, Claude Code, and Codex) to query details about Akib's skills, experience, projects, education, and career aspirations.

To maximize context-window efficiency and execution speed, the server implements Dynamic Context Retrieval (DCR), selectively parsing and returning only relevant sections of Akib's profile knowledge base instead of dumping the entire profile into the context.

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🚀 Features

• Dynamic Context Retrieval (DCR): Implements keyword tokenization and weight-based section scoring to rank and retrieve only the most relevant headings/sections matching the query.
• MCP Tools: Exposes a query_profile tool that takes natural language queries and returns optimized Markdown snippets.
• MCP Resources: Maps all markdown files in the knowledge/ database to knowledge:// URIs, enabling client LLMs to read raw content when needed.
• MCP Prompts: Exposes an akib_profile_assistant prompt that pre-loads system prompt instructions, response policies, and optional topic-specific retriever context.
• TypeScript ESM Architecture: Clean, modular, typed codebase utilizing modern Node.js and ES Modules.

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📁 Folder Structure

├── knowledge/
│   ├── dcr/                 # DCR architecture specifications
│   │   ├── overview.md
│   │   ├── retrieval-strategy.md
│   │   ├── ranking-rules.md
│   │   ├── query-examples.md
│   │   ├── response-policy.md
│   │   └── limitations.md
│   ├── about.md             # Core bio, location, professional summary
│   ├── skills.md            # Technical and soft skills
│   ├── projects.md          # Akib's project history (ChainIT Pay, etc.)
│   ├── experience.md        # Work experience details (eSparkBiz)
│   ├── education.md         # Saurashtra University degree
│   ├── career-goals.md      # Professional goals
│   ├── interests.md         # Architecture, scalable systems, AI
│   └── faq.md               # Common questions & answers
├── src/
│   ├── retriever/
│   │   └── DynamicContextRetriever.ts # DCR query analysis and section ranking
│   ├── services/
│   │   └── KnowledgeService.ts        # Markdown loader and segment parser
│   ├── tools/
│   │   └── index.ts                   # Tool schema definitions and handlers
│   └── index.ts                       # Server entry point, transport & handlers
├── package.json
├── tsconfig.json
└── README.md                # Project documentation (this file)

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🛠️ Getting Started

Prerequisites

• Node.js (v18.0.0 or higher recommended)
• npm (v9.0.0 or higher)

Installation

Clone the repository and install the dependencies:

npm install

Build

Compile the TypeScript codebase to the build/ directory:

npm run build

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⚙️ Running the Server

Start the MCP Server on standard I/O (Stdio):

npm start

For development with hot-reloading:

npm run dev

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🔌 Client Configuration

To connect this server to commonly used MCP clients:

1. Claude Desktop

Add the server configuration to your claude_desktop_config.json (located at %APPDATA%\Claude\claude_desktop_config.json on Windows or ~/Library/Application Support/Claude/claude_desktop_config.json on macOS):

{
  "mcpServers": {
    "akib-profile-server": {
      "command": "node",
      "args": [
        "d:/text/jpg/Projects/knowledge/build/index.js"
      ],
      "env": {}
    }
  }
}

2. Cursor IDE

1. Open Cursor Settings -> Features -> MCP.
2. Click + Add New MCP Server.
3. Fill in the configuration:
   • Name: Akib Profile Server
   • Type: stdio
   • Command: node d:/text/jpg/Projects/knowledge/build/index.js
4. Click Save.

3. Claude Code (CLI)

Add the server using the mcp command:

claude mcp add akib-profile-server node d:/text/jpg/Projects/knowledge/build/index.js

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🔍 How to Test

Using the MCP Inspector

You can test the server interactively by running the MCP Inspector:

npx @modelcontextprotocol/inspector node build/index.js

Manual Verification via Stdio

You can verify the JSON-RPC interface by piping requests to stdin:

1. List Tools:

   echo {"jsonrpc":"2.0","method":"tools/list","id":1,"params":{}} | node build/index.js
   

2. Query Profile (DCR in action):

   echo {"jsonrpc":"2.0","method":"tools/call","id":2,"params":{"name":"query_profile","arguments":{"query":"What databases does Akib know?"}}} | node build/index.js
   

3. List Prompts:

   echo {"jsonrpc":"2.0","method":"prompts/list","id":3,"params":{}} | node build/index.js
   

4. Get Prompt:

   echo {"jsonrpc":"2.0","method":"prompts/get","id":4,"params":{"name":"akib_profile_assistant","arguments":{"topic":"skills"}}} | node build/index.js
   

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💡 Architectural Highlights

• Granular Parsing: KnowledgeService splits markdown files by headings, parsing them into indexable KnowledgeSection blocks. This ensures high-granularity DCR mapping.
• Smart Token & Synonym Routing: DynamicContextRetriever tokenizes query keywords, strips stopwords, and uses a keyword-to-file boost matrix combined with bidirectional heading/content string matches.
• Robust Path Security: Target resource URIs are normalized and restricted within the root knowledge/ directory to prevent directory traversal vulnerabilities.