🛠️ DevUtils MCP Server

36 everyday developer tools for any MCP-compatible AI assistant. Hashing, encoding, UUID generation, JWT decoding, JSON formatting, network tools, text utilities, and more — all local, no external APIs.

🎯 Why?

Every developer needs to hash strings, encode/decode data, generate UUIDs, decode JWTs, format JSON, calculate CIDR ranges, and convert timestamps every day. DevUtils MCP Server brings all of these tools directly into your AI assistant — works with Claude, Cursor, VS Code, Windsurf, and any other MCP-compatible client.

Think of it as busybox for developer tools — small, essential, and always useful.

📦 Quick Start

Option 1 — npx (no install)

npx devutils-mcp-server

Option 2 — Docker

# Pull and run
docker run -i --rm ghcr.io/paladini/devutils-mcp-server

# Or build locally
docker build -t devutils-mcp-server .
docker run -i --rm devutils-mcp-server

Option 3 — Local install

npm install -g devutils-mcp-server
devutils-mcp-server

⚙️ Client Setup

Claude Desktop

Add to ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows):

{
  "mcpServers": {
    "devutils": {
      "command": "npx",
      "args": ["devutils-mcp-server"]
    }
  }
}

Or with Docker:

{
  "mcpServers": {
    "devutils": {
      "command": "docker",
      "args": ["run", "-i", "--rm", "ghcr.io/paladini/devutils-mcp-server"]
    }
  }
}

Cursor

Add to your Cursor MCP settings (~/.cursor/mcp.json):

{
  "mcpServers": {
    "devutils": {
      "command": "npx",
      "args": ["devutils-mcp-server"]
    }
  }
}

VS Code (GitHub Copilot)

Add to your .vscode/mcp.json in the workspace, or to your user settings:

{
  "servers": {
    "devutils": {
      "type": "stdio",
      "command": "npx",
      "args": ["devutils-mcp-server"]
    }
  }
}

Windsurf

Add to ~/.codeium/windsurf/mcp_config.json:

{
  "mcpServers": {
    "devutils": {
      "command": "npx",
      "args": ["devutils-mcp-server"]
    }
  }
}

Docker MCP Toolkit (Docker Desktop)

If this server is available in the Docker MCP Catalog, you can enable it directly from Docker Desktop:

1. Open Docker Desktop → MCP Toolkit

2. Search for DevUtils

3. Click Enable

Local Development

npm install
npm run dev

🔧 Available Tools (36 total)

🔐 Hash Tools (6)

🔄 Encoding Tools (8)

🎲 Generator Tools (4)

🔑 JWT Tools (2)

📝 Formatter Tools (3)

🔢 Converter Tools (5)

🌐 Network Tools (2)

✏️ Text Tools (6)

🏗️ Architecture

src/
├── index.ts          # MCP server entry point (stdio transport)
└── tools/
    ├── hash.ts       # Cryptographic hash functions
    ├── encoding.ts   # Encode/decode utilities
    ├── generators.ts # ID and password generators
    ├── jwt.ts        # JWT decode and validation
    ├── formatters.ts # JSON formatting and querying
    ├── converters.ts # Data type and unit converters
    ├── network.ts    # Network calculation utilities
    └── text.ts       # Text analysis and manipulation

Tech Stack:

• TypeScript + Node.js 22

• @modelcontextprotocol/sdk — Official MCP SDK

• bcryptjs — Password hashing

• nanoid — Compact ID generation

• zod — Input validation

Zero external API dependencies. All tools run locally with no network calls.

🐳 Docker

The image uses a multi-stage build for minimal size:

1. Build stage: Compiles TypeScript on Node 22 Alpine

2. Runtime stage: Runs compiled JS on Node 22 Alpine as non-root user

# Build
docker build -t devutils-mcp-server .

# Test (send an MCP initialize request)
echo '{"jsonrpc":"2.0","id":1,"method":"initialize","params":{"protocolVersion":"2024-11-05","capabilities":{},"clientInfo":{"name":"test","version":"1.0.0"}}}' | docker run -i --rm devutils-mcp-server

❓ FAQ & Design Philosophy

Why MCP, and not just a library?

Valid criticism: If you're writing Python scripts and need to hash something, hashlib is 2 lines of code. Why run MCP overhead?

Answer: This server is optimized for AI agents in multi-step workflows, not programmers writing code:

1. AI hallucination cost >> MCP overhead
   An AI model spending 50ms calling an MCP tool (vs. 1ms library call) is negligible when the alternative is the model making up a hash or using the wrong encoding. A wrong hash → debugging time → 1000x worse than overhead.

2. Reliable tool semantics
   Libraries let the model do anything (import, call, write loops). MCP enforces strict tool contracts. For example, jwt_decode always returns human-readable dates with timezone support — no model confusion about Unix epoch interpretation.

3. Universally accessible
   Any MCP-compatible client (Claude, Cursor, VS Code Copilot, Windsurf, and more) can use these tools. A Python library only works if your agent is Python-based.

4. Multi-tenant safety
   In production systems, letting AI agents run arbitrary library code is a security risk. MCP provides explicit tool whitelisting with input validation.

When to use DevUtils versus alternatives

Use DevUtils if:

• You're using Claude, Cursor, VS Code Copilot, Windsurf, or any MCP-compatible AI assistant

• You want reliable, validated utility operations in your AI workflows

• You need 36+ tools in one package (vs. learning 8 different tool specs)

• You want educational reference implementations of common algorithms

Don't use DevUtils if:

• You're writing regular Python/Node/Go code (use native libraries like hashlib, crypto)

• You need extreme performance (direct library calls are 1000x faster)

• Your AI client does not support MCP

Design philosophy

• Small & focused: 36 utilities, zero external APIs, ~50MB container

• Security-first: Non-root user, Alpine Linux, minimal attack surface

• AI-friendly: Consistent naming (<domain>_<operation>), strict schemas, human-readable outputs

• Client-agnostic: Works with any MCP-compatible client via stdio transport

• Battle-tested: Each tool references standard implementations (zod validation, bcryptjs hashing, etc.)

📝 Contributing

1. Fork the repository

2. Create your feature branch (git checkout -b feat/amazing-tool)

3. Commit your changes (git commit -m 'feat: add amazing tool')

4. Push to the branch (git push origin feat/amazing-tool)

5. Open a Pull Request

📄 License

MIT © Fernando Paladini