🛠️ DevUtils MCP Server

A comprehensive developer utilities toolkit for the Docker MCP Catalog. 36 everyday tools — hashing, encoding, UUID generation, JWT decoding, JSON formatting, network tools, text utilities, and more.

🎯 Why?

The Docker MCP Catalog has 200+ servers for databases, APIs, monitoring, and productivity — but no basic developer utilities toolkit.

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.

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

📦 Quick Start

With Docker

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

# Run (interactive, for MCP clients)
docker run -i --rm devutils-mcp-server

With Claude Desktop

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "devutils": {
      "command": "docker",
      "args": ["run", "-i", "--rm", "devutils-mcp-server"]
    }
  }
}

With 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

4. It will be available to any connected MCP client (Claude, Cursor, VS Code, etc.)

Local Development (without Docker)

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
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 AI client (Claude, Cursor, VS Code, ChatGPT plugins) 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, ChatGPT, or other AI agents

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

• You need 36+ tools in one container (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)

• You're in an environment without Docker/container support

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

• 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