MCP Toolkit Server

Overview

The MCP Toolkit Server is a production-ready Model Context Protocol (MCP) server that equips Claude, ChatGPT, and other LLM agents with a rich set of tools for interacting with databases, external APIs, the file system, and more — directly relevant to the agentic AI wave.

Built with TypeScript and the official @modelcontextprotocol/sdk, this server runs as a local stdio process and integrates seamlessly with Claude Desktop, the MCP Inspector, or any MCP-compatible client.

Features & Tools

Quick Start

Prerequisites

• Node.js >= 18.0.0

• npm >= 9.0.0

Installation

# Clone the repository
git clone https://github.com/vyshnavi-nandyala/mcp-toolkit-server.git
cd mcp-toolkit-server

# Install dependencies
npm install

# Build the TypeScript project
npm run build

Configure Claude Desktop

Add the server to your Claude Desktop configuration file:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json Windows: %APPDATA%\Claude\claude_desktop_config.json Linux: ~/.config/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "toolkit": {
      "command": "node",
      "args": ["/absolute/path/to/mcp-toolkit-server/dist/index.js"]
    }
  }
}

Replace /absolute/path/to/mcp-toolkit-server with the actual path on your machine.

Restart Claude Desktop, and you'll see a 🔨 icon in the input area — your tools are ready!

Using with MCP Inspector (Debugging)

npx @modelcontextprotocol/inspector node dist/index.js

This opens a web UI where you can manually test each tool, inspect request/response payloads, and debug issues.

Usage Examples

DB Query — Explore the Demo Database

Ask Claude:

"Show me the top 5 products by price from the demo database."

Claude will use the db_query tool:

{
  "sql": "SELECT name, category, price FROM products ORDER BY price DESC LIMIT 5"
}

API Call — Fetch Weather Data

Ask Claude:

"What's the current weather in San Francisco?"

Claude will use the api_call tool:

{
  "url": "https://api.open-meteo.com/v1/forecast?latitude=37.7749&longitude=-122.4194¤t_weather=true",
  "method": "GET"
}

File Operations

Ask Claude:

"List all TypeScript files in my project, then read the main entry point."

Claude will chain file_list → file_read:

{ "dirPath": "/path/to/project", "extension": ".ts", "recursive": true }
{ "filePath": "/path/to/project/src/index.ts" }

JSON Parsing

Ask Claude:

"Parse this JSON and extract the first user's email: {\"users\":[{\"email\":\"alice@example.com\"},{\"email\":\"bob@example.com\"}]}"

{
  "json": "{\"users\":[{\"email\":\"alice@example.com\"}]}",
  "operation": "query",
  "path": "users[0].email"
}

Text Transform

Ask Claude:

"Convert this to camelCase and slug: 'My Project Name'"

{ "text": "My Project Name", "operation": "camelcase" }
// → "myProjectName"

{ "text": "My Project Name", "operation": "slug" }
// → "my-project-name"

Architecture

mcp-toolkit-server/
├── src/
│   ├── index.ts                  # Entry point — creates and starts the MCP server
│   ├── tools/
│   │   ├── db-query.ts           # SQLite query tool (explore + file modes)
│   │   ├── api-call.ts           # HTTP request tool (fetch-based)
│   │   ├── file-operations.ts    # file_read, file_write, file_list
│   │   ├── calculator.ts         # Safe math expression evaluator
│   │   ├── datetime.ts           # Date/time with timezone support
│   │   ├── json-parser.ts        # Parse, query, validate, summarize JSON
│   │   ├── text-transform.ts     # 17+ text manipulation operations
│   │   └── environment.ts        # System environment info
│   └── utils/
│       └── helpers.ts            # Shared response-building utilities
├── tests/
│   └── tools.test.ts             # Unit tests (vitest)
├── package.json
├── tsconfig.json
└── README.md

Design Principles

1. Safety First — SQL injection prevention, no eval(), read-only defaults for DB queries

2. Modular — Each tool is a self-contained module; easy to add/remove tools

3. Typed — Full TypeScript with Zod schemas for input validation

4. Observable — Structured JSON responses with metadata (timing, counts, types)

5. Developer-Friendly — MCP Inspector support, comprehensive README, unit tests

Adding Custom Tools

Adding a new tool is straightforward:

// src/tools/my-custom-tool.ts
import { McpServer } from "@modelcontextprotocol/sdk/server/mcp.js";
import { z } from "zod";

export function registerMyCustomTool(server: McpServer): void {
  server.tool(
    "my_custom_tool",
    "Description of what this tool does.",
    {
      param1: z.string().describe("First parameter."),
      param2: z.number().optional().describe("Optional second parameter."),
    },
    async ({ param1, param2 }) => {
      // Your logic here
      return {
        content: [
          { type: "text", text: JSON.stringify({ result: "..." }, null, 2) },
        ],
      };
    }
  );
}

Then register it in src/index.ts:

import { registerMyCustomTool } from "./tools/my-custom-tool.js";
// ...
registerMyCustomTool(this.server);

Development

# Run in development mode (no build step needed)
npm run dev

# Build for production
npm run build

# Run tests
npm test

# Watch tests
npm run test:watch

# Lint
npm run lint

Why This Matters: The Agentic AI Wave

MCP (Model Context Protocol) is the open standard that allows AI agents like Claude to interact with external tools, data sources, and services. Instead of being confined to a chat window, MCP servers give agents the ability to:

• Query databases with natural language

• Call external APIs to fetch real-time data

• Read and write files on the local filesystem

• Perform computations and data transformations

• Compose multi-step workflows by chaining tools together

This server is a concrete, production-ready implementation of that vision — a toolkit that transforms Claude from a conversational AI into an actionable agent capable of interacting with the real world.

License

MIT License. See LICENSE for details.